Recent advances in microscopy imaging and image analysis motivate more and more institutes world-wide to establish dedicated core-facilities for bio-image analysis. To maximize the benefits research groups at these institutes gain from their core-facilities, they should be established to fit well into their respective environment. In this article, we introduce common collaborator requests and corresponding potential services core-facilities can offer. We also discuss potential conflicts of interests between the targeted missions and implementations of services to guide decision makers and core-facility founders to circumvent common pitfalls.

Convolutional neural network architectures have become increasingly complex, which has improved the performance slowly on well-known benchmark datasets in the recent years. In this research, we have analyzed the true need for such complexity. We have introduced G-Net light, a lightweight modified GoogleNet with improved filter count per layer to reduce feature overlaps and complexity. Additionally, by limiting the amount of pooling layers in the proposed architecture, we have exploited the skip connections to minimize the spatial information loss. The investigations on the proposed architecture are evaluated on three retinal vessel segmentation publicly available datasets. The proposed G-Net light outperforms other vessel segmentation architectures by reducing the number of trainable parameters..

The major goal of this paper is to use image enhancement techniques for enhancing and extracting data in FANET applications to improve the efficiency of surveillance. The proposed conceptual system design can improve the likelihood of FANET operations in oil pipeline surveillance, and sports and media coverage with the ultimate goal of providing efficient services to those who are interested. The system architecture model is based on current scientific principles and developing technologies. A FANET, which is capable of gathering image data from video-enabled drones, and an image processing system that permits data collection and analysis are the two primary components of the system. Based on the image processing technique, a proof of concept for efficient data extraction and enhancement in FANET situations and possible services is illustrated

The classification of vessel types in SAR imagery is of crucial importance for maritime applications. However, the ability to use real SAR imagery for deep learning classification is limited, due to the general lack of such data and/or the labor-intensive nature of labeling them. Simulating SAR images can overcome these limitations, allowing the generation of an infinite number of datasets. In this contribution, we present a synthetic SAR imagery dataset with ship wakes, which comprises 46080 images for ten different real vessel models. The variety of simulation parameters includes 16 ship heading directions, 6 ship velocities, 8 wind directions, 2 wind velocities, and 3 incidence angles. In addition, we extensively investigate classification performance for noise-free, noisy, and denoised ship wake scenes. We utilize the standard AlexNet architecture and employ training from scratch. To achieve the best classification performance, we conduct Bayesian optimization to determine hyperparameters. Results demonstrate that the classification of vessel types based on their SAR signatures is highly efficient, with maximum accuracies of 96.16%, 92.7%, and 93.59%, when training using noise-free, noisy, and denoised datasets respectively. Thus, we conclude that the best strategy in practical applications should be to train convolutional neural networks on denoised SAR datasets. The results show that the versatility of the SAR simulator can open up new horizons in the application of machine learning to a variety of SAR platforms.

Segmentation of brain tumor images is a major research topic in medical imaging to have a refined detection and understanding of abnormal masses in the brain. This paper proposes a new segmentation method, consisting of three main steps, to detect brain lesions using magnetic resonance imaging (MRI). In the first step, the parts of the image delineating the skull bone are removed to exclude insignificant data. In the second step, which is the main contribution of this study, the particle swarm optimization (PSO) technique is applied to detect the block that contains the brain lesions. The fitness function, used to determine the best block among all candidate blocks, is based on a two-way fixed-effects analysis of variance (ANOVA). In the last step of the algorithm, the K-means segmentation method is used in the lesion block to classify it as tumor or not. A thorough evaluation of the proposed algorithm is performed using the MRI database provided by the Kouba imaging center in Algiers, Algeria. Estimates of the selected fitness function are first compared to those based on the sum-of-absolute-differences (SAD) dissimilarity criterion and demonstrate the efficiency and robustness of the ANOVA. The performance of the optimized brain tumor segmentation algorithm is then compared to the results of several state-of-the-art techniques, including fuzzy C-means, K-means, Otsu thresholding, local thresholding, and watershed segmentation. The results obtained using Dice coefficient, Jaccard distance, correlation coefficient, and root mean square error (RMSE) measurements demonstrate the superiority of the proposed optimized segmentation algorithm over equivalent techniques.

Problem. Image biomarker analysis, also known as radiomics, is a tool for tissue characterization and treatment prognosis that relies on routinely acquired clinical images and delineations. Due to the uncertainty in image acquisition, processing, and segmentation (delineation) protocols, radiomics often lacks reproducibility. Radiomics harmonization techniques have been proposed as a solution to reduce these sources of uncertainty and/or their influence on the prognostic model performance. A relevant question is how to estimate the protocol-induced uncertainty of a specific image biomarker, what the effect is on the model performance, and how to optimize the model given the uncertainty. In this manuscript, we show how protocol uncertainty can drastically reduce prognostic model performance. We introduce an effect-size measure η that assesses the protocol-induced uncertainty versus the measurable effect. Methods. Two non-small cell lung cancer (NSCLC) cohorts, composed of 421 and 240 patients respectively, were used for training and testing. Per patient, a Monte Carlo algorithm was used to generate three hundred synthetic contours with a surface dice tolerance measure less than 1.18 mm with respect to the original GTV. These contours were subsequently used to derive 104 radiomic features, which were ranked on their relative sensitivity to contour perturbation, expressed in the parameter η. The top four (low η) and the bottom four (high η) features were selected for two models based on Cox proportional hazards model. To investigate the influence of segmentation uncertainty on the prognostic model, we trained and tested the setup in 5000 augmented realizations (using a Monte Carlo sampling method); the log-rank test was used to assess the stratification performance and stability to segmentation uncertainty. Results. Although both low and high η setup showed significant testing set log-rank p-values (p=0.01) in the original GTV delineations (without segmentation uncertainty introduced), in the model with high uncertainty to effect ratio only around 30% of the augmented realizations resulted in model performance with p < 0.05 in the test set. In contrast, the low η setup performed with log-rank p < 0.05 in 90% of the augmented realizations. Moreover, the high η setup classification was uncertain for 50% of the subjects in the testing set (for 80% agreement rate), whereas the low η setup was uncertain only in 10% of the cases. The code and part of the data are available at https://github.com/Maastro-CDS-Imaging-Group/sure. Discussion. Estimating image biomarker model performance based only on the original GTV segmentation without considering segmentation uncertainty may be deceiving. The model might result in a significant stratification performance, but can be unstable for delineation variations, which are inherent to manual segmentation. Simulating segmentation uncertainty using the method described allows for more stable image biomarker estimation, selection, and model development. The segmentation uncertainty estimation method described here is universal and can be extended to estimate other protocol uncertainties (such as image acquisition and pre-processing).

Body-Esteem Scale is an assessment tool for adolescents and adults that evaluate three dimensions of self-evaluations of one’s body. Body-Esteem Scale has been translated and validated in some countries since America down to Europe. Lack of translation and reliability evidence in Portugal was detected. This study aimed to translate and test the validity and reliability of the Body Esteem Scale for Adolescents and Adults (BESAA) in students in the context of Portuguese higher educa-tion. A total of 173 students (60.7% are female) with a mean age of 19.7 (standard deviation = 2.2) years participated. Categorical Principal Component Analysis was used to assess the underlying dimensions of BESAA. Construct validity was evaluated through correlation with the Appearance Schemas Inventory – Revised and a three-factor model (“Appearance”, ‘‘Weight’’ and “Attribu-tion’’) was established. Confirmatory factor analysis was performed to verify the construct validity of the instrument. Items that had factor weights (λ)<.40 were removed, as well as those that were considered redundant by the modification indices estimated by the Lagrange Multipliers (LM) method (LM>11, p<.001). We observed high correlations between theoretically similar factors, and low correlations between different factors. The Portuguese BESAA showed adequate validity and reliability.

Hydraulic conductivity is the key and one of the most uncertain parameters in groundwater modeling. The grid based numerical simulation require spatial distribution of sampled hydraulic conductivity at un-sampled locations in the study area. This spatial interpolation has been routinely performed using variogram based models (two-point geostatistics methods). These traditional techniques fail to capture the complex geological structures, provides smoothing effects and ignore the higher order moments of subsurface heterogeneities. In this work, a multiple-point geostatistics (MPS) method is applied to interpolate hydraulic conductivity data which will be further used in WASH123D numerical groundwater simulation model for regional smart groundwater management. To do this, MPS need ‘training images (TIs) as a key input. TI is a conceptual model of subsurface geological heterogeneity which was developed by using concept of ages, topographic slope as an index criteria and knowledge of geologist. After considerations of full physics of study area, an example shows the advantages of using multiple-point geostatistics compared with the traditional two-point geostatistics methods (such as Kriging) for the interpolation of hydraulic conductivity data in a complex geological formation.

The effect of the strain rate on damage in carbon black filled EPDM stretched during single and multiple uniaxial loading is investigated. This has been performed by analysing the stress-strain response, the evolution of damage by Digital Image Correlation (DIC), the associated dissipative heat source by InfraRed thermography (IR), and the chains network damage by swelling. The strain rates were selected to cover the transition from quasi-static to medium strain rate conditions. In single loading conditions, the increase of the strain rate yields in a preferential damage of the filler network while rubber network is preserved. Such damage is accompanied by a stress softening and an adiabatic heat source rise. Conversely, increasing the strain rate in cyclic loading conditions yields in a filler network accommodation and a high self-heating whose combined effect is proposed as a possible cause of the ability of filled EPDM to limit damage, by reducing cavities opening during loading and favoring cavities closing upon unloading.

Image fusion is a useful tool for producing a high-resolution multispectral image to be used for land use and land cover mapping. In this study, we use nine pansharpening algorithms namely Color Normalized (CN), Gram-Schmidt (GS), Hyperspherical Color Space (HCS), High Pass Filter (HPF), Nearest-Neighbor Diffusion (NND), Principal Component Analysis (PCA), Resolution Merge (RM), Stationary Wavelet Transform (SWT), and Wavelet Resolution Merge (WRM) to fusion Worldview-3 multispectral Bands and panchromatic band. In spectral and spatial fidelity, several image quality metrics are used to evaluate the performance of pansharpening algorithms. The SWT and PCA algorithms showed better results compared to other pansharpening algorithms while GS and CN algorithms showed the worst results for the original image fusion. The effect of fusion on each band was separately investigated and according to the calculations, we found that the CoastalBlue band and the Blue band showed the best result and the NIR-1 band and NIR-2 band show the worst result for the original image fusion. In the end, we conclude that the choice of fusion method depends on the requirement of remote sensing application.

In research applications across several areas, noise removal is indispensable for accuracy of final results. Noise is caused due to physical principals, such as background electronic noise, quantum effect, and wave rebound effect to name a few. Noise removal can help improve results in medical, astronomy, defense, and numerous other fields. Addressing this limitation would result in potentially low cost, automatic, and reliable systems. In this paper, a generalized new approach i.e. Multi-Frame Noise Removal (MFNR) is proposed for noise removal. Given any type of data, the probability density function (PDF) of the noise can be determined. Herein, we extracted the noise PDF parameters using KDE (Kernel Density Estimation). Because the data is corrupted by “deterministic” noise, hence can be cleaned. This could be used as a general purpose noise removal tool. The data point with same position in multiple frames helps us determine the noise PDF characteristics and hence making it possible to remove noise. The conventional wisdom which states that noise removal and detail preservation are contrary to each other is not true for MFNR. Experimental results validate our proposed method which showed practically complete noise reduction based on number of frames used, as compared to existing benchmark methods.

In this paper, our goal is to perform a virtual restoration of an ancient coin from its image. The present work is the first one to propose this problem, and it is motivated by two key promising applications. The first of these emerges from the recently recognised dependence of automatic image based coin type matching on the condition of the imaged coins; the algorithm introduced herein could be used as a pre-processing step, aimed at overcoming the aforementioned weakness. The second application concerns the utility both to professional and hobby numismatists of being able to visualise and study an ancient coin in a state closer to its original (minted) appearance. To address the conceptual problem at hand, we introduce a framework which comprises a deep learning based method using Generative Adversarial Networks, capable of learning the range of appearance variation of different semantic elements artistically depicted on coins, and a complementary algorithm used to collect, correctly label, and prepare for processing a large numbers of images (here 100,000) of ancient coins needed to facilitate the training of the aforementioned learning method. Empirical evaluation performed on a withheld subset of the data demonstrates extremely promising performance of the proposed methodology and shows that our algorithm correctly learns the spectra of appearance variation across different semantic elements, and despite the enormous variability present reconstructs the missing (damaged) detail while matching the surrounding semantic content and artistic style.

The notion of enhancement of the image is to ameliorate the perceptibility of information contained in an image. In the present research, a novel technique for the enhancement of image quality is propounded using fuzzy logic technique with a cuckoo optimization algorithm. Generally, the image is transformed from RGB domain to HSV domain keeping the color information intact within the image. The image has been categorized into three regions: underexposed, overexposed and mixed region on the basis of two threshold values. For the fuzzification of under and overexposed area the degree of membership is defined by the Gaussian membership, while the mixed area is fuzzified by parametric sigmoid function. The key parameters like visual factors and fuzzy contrast provide the quantitative analysis of an image. An objective function is framed which involves entropy and visual factor has been optimized by a new evolutionary cuckoo optimization algorithm. The results procured after simulation by the cuckoo optimization algorithm are compared with Bacterial foraging algorithm and ant colony optimization based image enhancement and this approach is found to be improved.

Identifying the total number of fruits on trees has long been of interest in agricultural crop estimation work. Yield prediction of fruits in practical environment is one of the hard and significant tasks to obtain better results in crop management system to achieve more productivity with regard to moderate cost. Utilized color vision in machine vision system to identify citrus fruits, and estimated yield information of the citrus grove in-real time. Fruit recognition algorithms based on color features to estimate the number of fruit. In the current research work, some low complexity and efficient image analysis approach was proposed to count yield fruits image in the natural scene. Semi automatic segmentation and yield calculation of fruit based on shape analysis is presented. Color and shape analysis was utilized to segment the images of different fruits like apple, pomegranate obtained under different lighting conditions. First the input sectional tree image was converted from RGB colour space into the colour space transform (i.e., YUV, YIQ, or YCbCr). The resultant image was then applied to the algorithm for fruit segmentation. After it is applied Morphological Operations which is enhanced image then execute Blob counting method which identify the object and count the number of it. Accuracy of this algorithm used in this thesis is 82.21% for images that have been scanned.

This study aims to clarify the influence of photographic environments under different light sources on image-based SPAD value prediction. Radish leaf patches of 1.5 cm diameter were photographed under halogen or LED light. The input variables for the SPAD value prediction using random forests were RGB values, HSL values, and HSV values. Additionally, the light color temperature (LCT) and illuminance (ILL) were used as the input variables. Model performance was assessed using Pearson’s correlation coefficient (COR), Nash-Sutcliffe efficiency (NSE), and root mean squared error (RMSE). SPAD value prediction resulted in high accuracy in a stable light environment; CORRGB+ILL+LCT and CORHSL+ILL+LCT were 0.929 and 0.922, respectively. Image-based SPAD value prediction was effective under halogen light with a similar color temperature at dusk; CORRGB+ILL and CORHSL+ILL were 0.895 and 0.876, respectively. The HSL value under LED could be used to predict the SPAD value with high accuracy; COR, NSE, and RMSE were 0.972, 0.944, and 2.07, respectively. The partial dependence plots of the H value indicate a change from blue to green with increasing SPAD values. Further studies are required to examine this method under outdoor conditions in spatiotemporally dynamic light environments.

Early detection of cervical cancer is the need of the hour to stop the fatality from this disease. There have been various CAD approaches in the past that promise to detect cervical cancer in an early stage, each of them having some constraints.This study proposes a novel ensemble deep learning model, VLR (variable learning rate), aimed at enhancing the accuracy of cervical cancer classification. The model architecture integrates VGG16, Logistic Regression, and ResNet50, combining their strengths in an offbeat ensemble design. VLR learns in two ways: firstly, by dynamic weights from three base models, each of them trained separately; secondly, by attention mechanisms used in the dense layer of the base models. Hyperparameter tuning is applied to further reduce loss, fine tune the model’s performance, and maximize classification accuracy. We performed K-fold cross-validation on VLR to evaluate any improvements in metric values resulting from hyperparameter fine tuning. We have also validated our model on images captured in three different solutions and on a secondary dataset. Our proposed VLR model outperformed existing methods in cervical cancer classification, achieving a remarkable training accuracy of 99.95% and a testing accuracy of 99.89%.

Early detection and precise characterization of brain tumors play a crucial role in improving patient outcomes and extending survival rates. Among neuroimaging modalities, magnetic resonance imaging (MRI) is the gold standard for brain tumor diagnostics due to its ability to produce high-contrast images across a variety of sequences, each highlighting distinct tissue characteristics. This study focuses on enabling multimodal MRI sequences to advance the automatic segmentation of low-grade astrocytomas, a challenging task due to their diffuse and irregular growth patterns. A novel mutual-attention deep learning framework is proposed, which integrates complementary information from multiple MRI sequences, including T2-weighted and fluid-attenuated inversion recovery (FLAIR) sequences, to enhance the segmentation accuracy. Unlike conventional segmentation models, which treat each modality independently or simply concatenate them, our model introduces mutual attention mechanisms. This allows the network to dynamically focus on salient features across modalities by jointly learning interdependencies between imaging sequences, leading to more precise boundary delineations even in regions with subtle tumor signals. The proposed method is validated using the UCSF-PDGM dataset, which consists of 35 astrocytoma cases, presenting a realistic and clinically challenging dataset. The results demonstrate that T2w/FLAIR modalities contribute most significantly to the segmentation performance. The mutual-attention model achieves an average Dice coefficient of 0.87, outperforming traditional approaches. This study provides an innovative pathway toward improving segmentation of low-grade tumors by enabling context-aware fusion across imaging sequences. Furthermore, the study showcases the clinical relevance of integrating AI with multimodal MRI, potentially improving non-invasive tumor characterization and guiding future research in radiological diagnostics.

The adversarial robustness of image quality assessment (IQA) models to adversarial attacks is emerging as a critical issue. Adversarial training has been widely used to improve the robustness of neural networks to adversarial attacks, but little in-depth research has examined adversarial training as a way to improve IQA model robustness. This study introduces an enhanced adversarial training approach tailored to IQA models; it adjusts the perceptual quality scores of adversarial images during training to enhance the correlation between an IQA model’s quality and the subjective quality scores. We also propose a new method for comparing IQA model robustness by measuring the Integral Robustness Score; this method evaluates the IQA model resistance to a set of adversarial perturbations with different magnitudes. We used our adversarial training approach to increase the robustness of five IQA models. Additionally, we tested the robustness of adversarially trained IQA models to 16 adversarial attacks and conducted an empirical probabilistic estimation of this feature. The code is available at https://github.com/wianluna/metrics_at.

The evaluation of wrinkles in deep-drawn fiber-based materials is crucial for the assessment of product quality and the optimization of manufacturing processes. Wrinkles, a common phenomenon in deep-drawing paper materials with high drawing depths, significantly impair the appearance and mechanical properties of the final product. The objective of this study is to identify the key process parameters affecting wrinkling and to deepen the understanding of their roles and interactions using wrinkle data for deep-drawn paper products. Image analysis techniques are employed, supported by a specially constructed darkroom platform to ensure uniform light intensity for capturing photographs. An automated program is developed for the detection and evaluation of wrinkle characteristics and distribution, which allows the free choice of the region to be detected and the representation of the wrinkle geometry not limited by the number. To enhance the precision of this program, the ellipticity is initially rectified for products without flanges, specifically cup-shaped deep-drawn products. The ellipticity is caused by the pronounced springback effect of the paperboard. The approach is employed to investigate the impact of material properties, blank holder force, drawing depth, drawing clearance, and punch speed on wrinkling formation after the deep drawing process. The findings reveal that the blank holder force and drawing clearance are critical factors in wrinkle formation, with higher forces generally leading to increased wrinkle numbers.

This review provides an overview of eXplainable AI (XAI) methods for oncological ultrasound image analysis and compares their performance evaluations. A systematic search of Medline Embase and Scopus between March 25 and April 14 2024 identified 17 studies describing 14 XAI methods, including visualization, semantics, example-based, and hybrid functions. These methods primarily provided specific, local, and post-hoc explanations. Performance evaluations focused on AI model performance, with limited assessment of explainability impact. Standardized evaluations incorporating clinical end-users are generally lacking. Enhanced XAI transparency may facilitate AI integration into clinical workflows. Future research should develop real-time methodologies and standardized quantitative evaluative metrics.

The primary objective of this study was to demonstrate the potential of digital image analysis as a tool to identify microplastic (MP) particles in surface waters and to facilitate their characterisation in terms of 2D and 3D morphology. Digital image analysis preceded by microscopic analysis was used for an exhaustive quantitative and qualitative evaluation of MPs isolated from the Vistula river. Using image processing procedures, 2D and 3D shape descriptors were determined. The Principal Components Analysis (PCA) was used to interpret the relationships between the parameters studied, characterising MP particle geometry, type and colour. This multivariate analysis of the data allowed 3 or 4 main factors to be extracted, explaining approximately 90% of the variation in the data characterising MP morphology. It was found that the first principal component for granules, flakes and films was largely represented by strongly correlated with 2D shape descriptors (area, perimeter, equivalent area diameter) and 3D shape descriptors (CSF, Compactness, Dimensionality). Considering the scraps, PC1 was represented by only five of the above descriptors, and the Compactness variable had the largest contribution to PC2. In addition, for granules, flakes and films, a relationship between 2D shape and the colour of their particles could be observed. For the most numerous MP group identified of multicoloured scraps, no such association was found. The results of our study can be used for further multivariate analysis regarding the presence of microplastic floating on the river surface, with a particular focus on particles of secondary origin. This is of key importance for optimising future efforts in conducting small-scale and multidimensional monitoring of and reducing plastics in the aquatic environment.

Microscopic and ultra-microscopic vascular sutures are indispensable in the surgical procedures such as arm transplantation and finger reattachment. The state of the blood vessels after suturing, such as vascular patency, narrow, and blocked, determines the success rate of the operation. If we can grasp the golden window period after blood vessel suture and before muscle tissue suture to achieve accurate and objective assessment of blood vessel status, it can not only reduce medical costs but also improve social benefits. Doppler optical coherence tomography enables high-speed, high-resolution imaging of biological tissues, especially microscopic and ultra-microscopic blood vessels. Using Doppler optical coherence tomography to image the sutured blood vessels, not only the three-dimensional structure of the blood vessels, but also the blood flow information can be obtained. By extracting the contour of blood vessel wall and the contour of blood flow area, the three-dimensional shape of blood vessel can be reconstructed in three dimensions, which provides parameter support for the assessment of blood vessel status. In this work, we propose a neural network-based multi-classification deep learning model that can simultaneously and automatically extract blood vessel boundaries from Doppler OCT vessel intensity images and the contours of blood flow regions from corresponding Doppler OCT vessel phase images. Compared with the traditional random walk segmentation algorithm and cascade neural network method, the proposed model has better performance. This method is easier to realize system integration and has great potential for clinical evaluation. It is expected to be applied to the evaluation of microscopic and ultra-microscopic vascular status in microvascular anastomosis.

The fusion of global contextual information with local cropped block details is crucial for segmenting ultra-high resolution images. In this study, we introduce a novel fusion mechanism termed Global-Local Deep Fusion (GL-Deep Fusion) based on an enhanced transformer architecture, which efficiently integrates global contextual information and local details. Specifically, we propose the Global-Local Synthesis Networks (GLSNet), a dual-branch network where one branch processes the entire original image, while the other branch handles cropped local patches as input. The feature fusion of different branches in GLSNet is achieved through GL-Deep Fusion, significantly enhancing the accuracy of ultra-high resolution image segmentation. Particularly effective is GLSNet in identifying tiny overlapping items. To optimize GPU memory utilization, we meticulously design a dual-branch architecture that proficiently leverages the features it extracts, seamlessly integrating them into the enhanced transformer framework of GL-Deep Fusion. Extensive experiments conducted on challenging benchmarks, including DeepGlobe and Vaihingen datasets, demonstrate that GLSNet achieves a new state-of-the-art performance in terms of GPU memory utilization and segmentation accuracy tradeoff.

Railway transportation is one of the main modes of long-distance coal transportation, and it is inevitable to cause environmental pollution during the transportation process. In order to improve the environment along the railway and increase the utilization rate of coal resources, this paper proposes a detection algorithm for scattered coal solidification layer during transportation based on the YOLO v8 model, and designs an intelligent recognition model suitable for coal solidification layer detection devices by combining fluorescence detection methods. Through experimental analysis, the model meets the requirements of practical detection and plays a crucial role in environmental protection, with strong practical application value.

As the fast growth of information technology (IT), the safety of image transmission and the storing of images are increasingly concerned. Traditional image encryption algorithms have certain limitations in transmission and security, so there is an urgent need for a secure and reliable image encryption algorithm. A universal compression sensing (CS) image encryption (IE) algorithm based on DNA-triploid mutation (DTM) is presented in this paper. Firstly, by using CS algorithm an image is compressed while obtaining a range of chaotic sequences by iteration of chaotic map. Then DNA sequences are generated by encoding the image, and based on the DTM, new mutant DNA sequences are generated according to specific rules. Next, the chaotic sequences are operated at the DNA level to do confusion and diffusion operations on the image to ensure the security of the data. At last, DNA decoding is carried out to obtain the compressed-encrypted image. Both simulation experiments and performance tests fully show that a high level of security and reliability of the proposed algorithm in protecting image privacy is achieved.

Neural-objective and image optimization approaches for drilling fluid rheology automation are crucial for drilling engineering optimization. A myriad of intelligent computational models are employed to predict and monitor the parameters of mud rheology and filter cake permeability posture using an artificial neural network feedforward (ANN-FF) function, a non-ANN-FF function, an image processing tool, and a model optimization tool. 498 datasets of synthetic-based mud (SBM) flat rheology from a drilling mud laboratory at Nazarbayev University were imported into a user-friendly MATLAB application using image processing and nftool. Apart from the Google TensorFlow Sequential API DNN architecture used, a Levenberg-Marquardt training algorithm with input sigmoid hidden neurons 10, 12, and 18 coupled with a linear output layer was also used to predict the SBM flow index. OBM and SBM filter cakes were processed for void spaces; however, a model study was extrapolated to maximize filtration volume Vf. The study’s findings show that the ANN-FF model employed for rheological property monitoring and prediction had higher steady and exponential levels of accuracy and a correlation coefficient of 0.96-0.99. More so, SBM and OBM image processing presented an area of void spaces of 1790M2 and 1739M2, respectively. The porosity and permeability postures of both SBM and OBM resulted in a significant void space capable of maximizing the flow index. In addition, the single-objective modelling based on a genetic algorithm did validate the experimental rheological data for flow index or filtration volume Vf optimization; the study resulted in the finding that the constrained physics-informed objective function hindered maximizing oil recovery and instead predicted possible formation damage. It is empirical to note that automating flow predictions with neural, objective, and image functions has presented an alternative novel method for non-programmers using MATLAB and Google Colabs that is capable of enhancing mud rheological parameters, drilling efficiency, and hydrocarbon recovery.

To ensure the security of highly sensitive remote sensing images (RSI) during their transmission, it is essential to implement effective content security protection methods. Generally, secure distribution schemes for remote sensing images often employ cryptographic techniques. However, sending encrypted data exposes communication behavior, which poses significant security risks to the distribution of remote sensing images. Therefore, this paper introduces deep information hiding to achieve secure distribution of remote sensing images, which can serve as an effective alternative in certain specific scenarios. Specifically, the Deep Information Hiding for RSI Distribution (hereinafter referred to as DIH4RSID) based on encoder-decoder network architecture with Parallel Attention Mechanisms (PAM) by adversarial training was proposed. Our model is constructed with four main components: a preprocessing network (PN), an embedding network (EN), a revealing network (RN) and a discriminating network (DN). The PN module is primarily based on Inception to capture more details of RSI and targets of different scales. The PAM module obtains features in two spatial directions to realize feature enhancement and context information integration. The experimental results indicate that our proposed algorithm achieves relatively higher visual quality and secure level compared to related methods. Additionally, after extracting the concealed content from hidden images, the average classification accuracy is unaffected.

To support pathologists in breast tumor diagnosis, deep learning plays a crucial role in the development of histological whole slide image (WSI) classification methods. However, automatic classification is challenging due to the high-resolution data and the scarcity of representative training data. To tackle these limitations, we propose a deep learning-based breast tumor gigapixel histological image multi-classifier integrated with a high-resolution data augmentation model to process the entire slide by exploring its local and global information and generating its different synthetic versions. The key idea is to perform classification and augmentation in feature latent space, reducing the computational cost while preserving the class label of the input. We adopt a deep learning-based multi-classification method and evaluate the contribution given by a Conditional Generative Adversarial Network-based data augmentation model on the classifier’s performance for three tumor classes in the BRIGHT Challenge dataset. The proposed method has allowed us to achieve an average F1 equal to 69.5, considering only the WSI dataset of the Challenge. The results are comparable to those obtained by the Challenge winning method (71.6), also trained on the annotated tumor region dataset of the Challenge.

Endoscopic medical images can suffer from uneven illumination, low contrast, and lack of texture information due to the use of point directional light sources and the presence of narrow tissue structures, posing diagnostic difficulties for physicians. In this paper, a deep learning-based su-pervised illumination enhancement network is designed for low-light endoscopic images, aiming to improve both global illumination and local details. Initially, a global illumination enhancement module is formulated utilizing a higher-order curve function to improve global illumination. Sec-ondly, a local feature extraction module incorporating dual attention is designed to capture local detailed features. Considering the significance of color fidelity in biomedical scenarios, the designed loss function prioritizes introducing color loss to alleviate image color distortion. Compared with seven state-of-the-art enhancement algorithms on Endo4IE endoscopic datasets, experimental re-sults show that the proposed method can better enhance low-light endoscopic images and avoid image color distortion. It provides an efficient method to enhance images captured by endoscopes which can effectively assist clinical diagnosis and treatment.

Aircraft heat exchangers play a crucial role in maintaining thermal balance and ensuring that essential components operate efficiently and safely. In this context, it is highly relevant to gain knowledge about the deterioration and the predominant damage mechanisms of these components. In this study, two different non-destructive methods are employed to analyze the damage initiation of a heat exchanger part during fatigue test. On the one hand, Acoustic Emission technique is employed using membrane-free microphones capable of capturing a broad bandwidth. The acoustic events were classified by a machine learning algorithm to determine their source and damage mechanisms. On the other side, Digital Image Correlation (DIC) allowed the measurement of the strain evolution along the test and more precisely, redistributions were considered as damage indicators. As a result, it was identified that AE served as early damage indicator as the cumulative number of events was in good agreement with the severity of the damage. With respect to classification two clear clusters ascribed to different type of events were identified. In the case of DIC, strain redistributions gave clear indications of damage or deterioration but at a later stage compared to AE

With the development of remote sensing satellite technology for Earth observation, remote sensing stereo images have been used for three-dimensional reconstruction in various fields, such as urban planning and construction. However, remote sensing images often include noise, occluded regions, weakly textured areas and repeated textures, which can lead to reduced accuracy in stereo matching and affect the quality of the 3D reconstruction results. To reduce the impact of complex scenes in remote sensing images on stereo matching and to ensure both speed and accuracy, we propose a new end-to-end stereo matching network based on convolutional neural networks (CNNs). The proposed stereo matching network can learn features at different scales from the original images and construct cost volumes with varying scales to obtain richer scale information. Additionally, when constructing the cost volume, we introduce negative disparity to adapt to the common occurrence of both negative and nonnegative disparities in remote sensing stereo image pairs. For cost aggregation, we employ a 3D convolution-based encoder-decoder structure that allows the network to adaptively aggregate information. Before feature aggregation, we also introduce an attention module to retain more valuable feature information, enhance feature representation, and obtain a higher-quality disparity map. By training on the publicly available US3D dataset, we obtain the accuracy that 1.115 pixel in end-point error (EPE) and 5.32% in the error pixel ratio (D1) on the test dataset, and the inference speed is 92 ms. Comparing our model with existing state-of-the-art models, we achieve higher accuracy, and the network is beneficial for the three-dimensional reconstruction of remote sensing images.

Multispectral image (MSI) and hyperspectral image (HSI) fusion (MHIF) aims to address the challenge of acquiring high-resolution (HR) HSI images. This field combines a low-resolution (LR) HSI with an HR-MSI to reconstruct HR-HSI. Existing methods directly utilize transformers to perform feature extraction and fusion. Despite the demonstrated success, there exist two limitations: 1) Employing the entire transformer model for feature extraction and fusion fails to fully harness the transformer’s potential in integrating the spectral of the HSI and spatial information of the MSI. 2) HSI has a strong spectral correlation and exhibits sparsity in the spatial domain. Existing transformer-based models do not optimize this physical property, which makes their methods prone to spectral distortion. To accomplish these issues, this paper introduces a novel framework for MHIF called Sparse Mix-Attention Transformer (SAMformer). Specifically, to fully harness the advantages of the Transformer architecture, we propose a Spectral Mix Attention Block (SMAB), which concatenates the keys and values extracted from LR-HSI and HR-MSI to create a new multi-head attention module. This design facilitates the extraction of detailed long-range information across spatial and spectral dimensions. Besides, to address the spatial sparsity inherent in HSI, we incorporated a sparse mechanism within the core of SMAB called Sparse Spectral Mix Attention Block (SSMAB). In the SSMAB, we compute attention maps from queries and keys and select the K highly correlated values as the sparse attention map. This approach enables us to achieve a sparse representation of spatial information while eliminating spatially disruptive noise. Extensive experiments conducted on three benchmark datasets, namely Cave, Harvard, and Pavia Center, demonstrate the SMAformer method outperforms state-of-the-art methods.

This paper explores the application of wavelet analysis, a multiscale approach for enhancing visual clarity, in the context of image denoising, which reduces the impact of noisy pixels, caused by various factors such as electronic sensor limitations, low-light conditions, or transmission errors in digital imaging systems. This paper introduces some common noise types like Gaussian noise and processes of image denoising, eliciting the strength of wavelet analysis. As a powerful image denoising technology, wavelet analysis needs five steps to process the image, and the key step is thresholding. There are many kinds of wavelet, and each wavelet has different advantages and functions, which makes it suitable for different applications. Hence, wavelet analysis also makes a contribution in various fields beyond signal processing and data analysis, such as Biomedical Imaging and Geophysics and Seismology, where preserving image quality is essential for accurate analysis and interpretation. In short, this research highlights the promise of wavelet analysis, emphasizing the use of high- quality image data.

Recent technological advances have significantly increased the data volume obtained from deep space exploration missions, making the downlink rate a primary limiting factor. Particularly, JAXA’s Martian Moons eXploration (MMX) mission encounters this problem when identifying safe and scientifically valuable landing sites on Phobos using high-resolution images. A strategic approach in which we effectively reduce image data volumes without compromising essential scientific information is thus required. In this work, we investigate the influence of image data compression, especially as it concerns the accuracy of generating the local Digital Terrain Models (DTMs) that will be used to determine MMX’s landing sites. We obtain simulated images of Phobos that are compressed using the CCSDS-120 with integer/float-point discrete wavelet transform (DWT), candidate algorithms for the MMX mission. Accordingly, we show that, if the compression ratio is 70% or lower, the effect of image compression remains constrained, and local DTMs can be generated within altitude errors of 40 cm on the surface of Phobos, which is ideal for selecting safe landing spots. We conclude that the compression ratio can be increased as high as 70%, and such compression enables us to facilitate critical phases in the MMX mission even with the limited downlink rate.

This study examines how the design of museum archaeological boxes and the overall museum image affect behavioral intention. This research used partial least squares-structural equation modeling, indicating a relationship between aesthetics, symbolism, affective museum image, perceived value, and behavioral intention. This study found that symbolism predicted behavioral intention. Moreover, aesthetics, symbolism, and affective museum image affected perceived value, in turn triggering behavioral intention. This indicates theoretical and practical implications for museum products.

For Synthetic Aperture Radar (SAR) image registration, successive processes following feature extraction are required by both the traditional feature-based method and the deep learning method. Among these processes, the feature matching process—whose time and space complexity are related to the number of feature points extracted from sensed and reference images, as well as the dimension of feature descriptors—proves to be particularly time-consuming. Additionally, the successive processes introduce data sharing and memory occupancy issues, requiring an elaborate design to prevent memory leaks. To address these challenges, this paper introduces the OptionEM-based reinforcement learning framework to achieve end-to-end SAR image registration. This framework outputs registered images directly without requiring feature matching and calculation of the transformation matrix, leading to significant processing time savings. The Transformer architecture is employed to learn image features, while a correlation network is introduced to learn the correlation and transformation matrix between image pairs. Reinforcement learning, as a decision process, can dynamically correct errors, making it more efficient and robust compared to supervised learning mechanisms like deep learning. We present a hierarchical reinforcement learning framework combined with episodic memory to mitigate the inherent problem of invalid exploration in generalized reinforcement learning algorithms. This approach effectively combines coarse and fine registration, further enhancing training efficiency. Experiments conducted on three sets of SAR images, acquired by TerraSAR-X and Sentinel-1A, demonstrate that the proposed method’s average runtime is sub-second, achieving subpixel registration accuracy.

The dependence on Unmanned Aerial Vehicles (UAVs) has dramatically increased in many sectors around the globe. UAVs are in high demand, and their technology is developing quickly due to their sophisticated ability to handle various issues. UAVs are capable of replacing labor-intensive tasks with conducive and safe regulation. Additional tools or sensors need to be added to the UAVs system to ensure the implementation of UAVs able to serve into industrial level. The paper aims to consolidate and present a thorough understanding of the various stages of image processing pipelines deployed in UAV applications, including image acquisition, preprocessing, feature extraction, object detection and tracking, and decision-making processes. Throughout this paper, several aspects were deliberate such as strengths, limitations, and performance metrics of existing approaches, this paper seeks to provide researchers, engineers, and practitioners with valuable insights into the challenges and opportunities of image processing systems for UAVs. Ultimately, the synthesis of this knowledge will contribute to enhancing the effective-ness, autonomy, and applicability of UAVs in diverse fields such as surveillance, agriculture, disaster management, and environmental monitoring.

River monitoring has the potential to grow substantially with present-day available affordable and locally sourced hardware. Yet river monitoring networks are still under pressure due to lack of resources, difficulties with maintenance, and rapidly changing conditions which in part may be due to climate change. We advocate that to turn around this trend, monitoring stations must rely on local people, work with locally available devices, work without contact with water and operate through openly available knowledge. River observations with camera videos have this potential. IP cameras, drones or smartphones are widely available as observation platforms. The scientific methods are well established in literature. Yet current attempts to establish scalable open-source software solutions that can be operated anywhere are lacking. In fact, currently available software is either research-oriented, more aimed at incidental observations, is restricted to single-use licenses, entirely proprietary, or restricted to operations through a third-party Software as a Service. To overcome this obstacle, we present OpenRiverCam, a free and open-source software ecosystem for river observations that can fulfill a wide variety of use cases and business cases through a well-documented and simple to use Application Programming Interface, service workflows, cloud scalability and interoperability, and options to extend to several applications within the hydrological, hydrodynamic, environmental and geospatial domains. We demonstrate its current technical abilities through three different case studies that all originate from a user perspective. We discuss the future developments to meet further requirements, which include documentation, widely available training materials and embedding in curricula, and further hardware and software developments.

Near Earth Asteroids represent potential threats to human life because their trajectories may bring them in the proximity of the Earth. Monitoring these objects could help predict future impact events, but such efforts are hindered by the large numbers of objects that pass through the Earth’s vicinity. Additionally, there is also the problem of distinguishing asteroids from other objects in the night sky, which implies sifting through large sets of telescope image data. Within this context, we believe that employing machine learning techniques could greatly improve the detection process by sorting out the most likely asteroid candidates to be reviewed by human experts. At the moment, the use of machine learning techniques is still limited in the field of astronomy and the main goal of the present paper is to study the effectiveness of deep CNNs for the classification of astronomical objects, asteroids in this particular case, by comparing some of the well-known deep convolutional neural networks, including InceptionV3, Xception, InceptionResNetV2 and ResNet152V2. We have applied transfer learning and fine-tuning on these pre-existing deep convolutional networks and from the results that we have obtained one can see the potential of using deep convolutional neural networks in the process of asteroid classification. The InceptionV3 model has the best results in the asteroid class, meaning that by using it, we loose the least number of valid asteroids.

The unique characteristics of frescoes on overseas Chinese buildings can attest to the integration and historical background of Chinese and Western cultures. Reasonable analysis and preservation of overseas Chinese frescoes can provide sustainable development for culture and history. This research adopts the image analysis technology based on artificial intelligence, and proposes a ResNet-34 model and method integrating transfer learning. This deep learning model can identify and classify the source of the frescoes of the emigrants, and can effectively deal with the problems such as the small number of fresco images on the emigrants' buildings, poor quality, difficulty in feature extraction, and similar pattern text and style. The experimental results show that the training process of the model proposed in this article is stable. On the constructed Jiangmen and Haikou fresco JHD datasets, the final accuracy is 98.41%, and the recall rate is 98.53%. The above evaluation indicators are superior to classic models such as AlexNet, GoogLeNet, and VGGNet. It can be seen that the model in this article has strong generalization ability and is not prone to overfitting. It can effectively identify and classify the cultural connotations and regions of frescoes.

The widespread availability of digital image processing softwares has given rise to various forms of image manipulation and forgery which can pose a significant challenge in different fields such as law enforcement, journalism etc,. it can also lead a privacy concern. We are proposing a privacy-preserving framework to encrypt images before processing them is vital to maintain the privacy and confidentiality of sensitive images, especially those used for purpose of Investigation. To address these challenges, we propose a novel solution that detects image forgeries while preserving the privacy of the images. Our method proposes a privacy-preserving framework that encrypts the images before processing them, making it difficult for unauthorized individuals to access them. The proposed method utilizes a compression quality analysis in the encrypted domain to detect the presence of forgeries in images if the forged portion (dummy image) has a compression quality different from the original image (featured image) in the encrypted domain. This approach effectively localizes the tampered portions of the image, even for small pixel blocks of size 10x10 in the encrypted domain. Furthermore, the method identifies the featured image's JPEG quality using the first minima in the energy graph.

Shape of supracondylar fracture of the humeral of pediatric patients is analysed with Procrustes method. XR–images of fractures are considered both in anterio-posterior (AP) view (66 images) and in a lateral (L) view (24 images). Applying Procrustes method for both views mean images are constructed and compared. Variability of shapes is quantified with a shape principal component analysis. Non-parametric tests (permutational and bootstrap) do not indicate statistical difference between Procrustes mean shapes in anterio-posterior and lateral projections. It is shown, however, that AP and L shapes of humeral fractures differ in their variability quantified by shape principal components.

CNN-based off-the-shelf features have shown themselves as a good baseline for trademark retrieval. However, in recent years, the computer vision area was transitioning from CNNs to a new architecture – Vision Transformer. In this paper, we investigate the performance of off-the-shelf features extracted with vision transformers and explore the effects of pre, post-processing, and pre-training on big datasets. We propose a method of joint usage of global and local features, which leverages the best aspects of both approaches. Experimental results on METU Trademark Dataset show that off-the-shelf features extracted with ViT-based models outperform off-the-shelf features from CNN-based models. The proposed method achieves the mAP value of 31.23, surpassing previous state-of-the-art results. We assume that the proposed approach for the trademark similarity evaluation will allow one to improve the protection of such data with the help of artificial intelligence methods. Moreover, this approach will allow one to identify cases of unfair use of such data and form an evidence base for litigation.

We propose an algorithm for generating a panoramic image of a pipe’s inner surface based on inverse perspective mapping (IPM). For efficient crack detection, we used only perspective images of the pipe interior that can be obtained with a single lens camera instead of those taken with a 360-degree camera or a 3D depth camera. Frontal images taken while passing through the pipe were converted to images of the inner surface of the pipe using IPM. We derived a generalized IPM formula that considers the slope of the image plane to correct image distortion caused by the tilt of the plane; this IPM formula was derived based on the vanishing point of the perspective image, which was detected using optical flow techniques. Finally, the multiple transformed images with overlapping areas were combined via image stitching to create a panoramic image of the inner pipe surface. To validate our proposed algorithm, we restored images of pipe inner surface using a 3D pipe model and used these images for crack detection.

The agricultural sector is undergoing a revolution that requires sustainable solutions to the challenges that arise from traditional farming methods. To address these challenges, technical and sustainable support is needed to develop projects that improve crop performance. This study focuses on the onion crop and the challenges presented throughout its phenological cycle. Aerial monitoring using unmanned aerial vehicles (UAV) and digital image processing were used to identify patterns in the onion crop, including humid areas, weed growth, vegetation deficits, and decreased harvest performance. An algorithm was developed to identify the patterns that most affected crop growth, as the average local production reported was 40.166 ton/ha, but only 25.00 ton/ha was reached due to blight caused by constant humidity and limited sunlight. This resulted in the death of leaves and poor development of bulbs, with 50% of the production being of medium size. It is estimated that approximately 20% of the production was lost due to blight and unfavorable weather conditions.

Translation of basic animal research to find effective methods of diagnosing and treating human neurological disorders requires parallel analysis infrastructures. Small animals such as mice provide exploratory animal disease models. However, many interventions developed using small animal models fail to translate to human use due to physical or biological differences. Recently, large-animal minipigs have emerged in neuroscience due to both brain similarity and economic advantages. Medical image processing is a crucial part of research as it allows researchers to monitor their experiments and understand disease development. However, although many algorithms are created and optimized for MR analysis of human data, those tools are not directly applicable or sufficiently sensitive to measure minipig data. In this work, we propose PigSNIPE - a pipeline for the automated handling, processing, and analyzing of large-scale data sets of minipig MR images. The pipeline allows for image registration, AC-PC alignment, landmark detection, skull stripping, brainmasks and intracranial volume segmentation (DICE 0.98), tissue segmentation (DICE 0.82), and caudate-putamen brain segmentation (DICE 0.8) in under two minutes. To the best of our knowledge, this is the first automated pipeline tool aimed at large animal images.

Intracranial hemorrhage is a serious medical problem that requires rapid and often intensive medical care. Identifying the location and type of any hemorrhage present is a critical step in the treatment of the patient. Diagnosis requires an urgent procedure, and the detection of hemorrhage is a difficult and time-consuming process for human experts. In this paper, we propose methods based on EfficientDet’s deep-learning technology that can be applied to the diagnosis of hemorrhages and thus become a decision-support system. Our proposal is two-fold. On the one hand, the proposed technique classifies slices of computed tomography scans for the presence hemorrhage or its lack, achieving 92.7% accuracy and 0.978 ROC-AUC. On the other hand, our methodology provides visual explanations of the classification chosen using the Grad-CAM methodology.

A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: describe briefly the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article, it must not contain results which are not presented and substantiated in the main text and should not exaggerate the main conclusions.

Although neural network architectures are critical for their performance, how the structural characteristics of a neural network affect its performance has still not been fully explored. We here map architectures of neural network to directed acyclic graphs, and find that incoherence, a structural characteristic to measure the order of directed acyclic graphs, is a good indicator for the performance of corresponding neural networks. Therefore we propose a deep isotropic neural network architecture by folding a chain of same blocks then connecting the blocks with skip connections at different distances. Our models, named FoldNet, have two distinguishing features compared with traditional residual neural netowrks. First, the distances between block pairs connected by skip connections increase from always equal to one to specially selected different values, which lead to more incoherent graphs and let the neural network explore larger receptive fields and thus enhance its multi-scale representation ability. Second, the number of direct paths increases from one to multiple, which leads to a larger proportion of shorter paths and thus improve the direct propagation of information throughout the entire network. Image classification results on CIFAR-10 and Tiny ImageNet benchmarks suggested that our new network architecture performs better than traditional residual neural networks.

Diatom identification and counting by light microscopy is a fundamental method in ecological and water quality investigations. Here we present a new variant of this method based on “digital virtual slides”, and compare it to the traditional, non-digitized light microscopy workflow. We analysed three replicates of six samples using two methods: 1) working directly on a light microscope (the “traditional” counting method), and 2) preparing “virtual digital slides” by high-resolution slide scanning and subsequently identifying and labelling individual valves or frustules using a web browser-based image annotation platform (the digital method). Both methods led to comparable results in terms of species richness, diatom indices and diatom community composition. Although counting by digital microscopy was slightly more time consuming, our experience points out that the digital workflow can not only improve the transparency and reusability of diatom counts but it can also increase taxonomic precision. The introduced digital workflow can also be applied for taxonomic inter-expert calibration through the web, and for producing training image sets for deep-learning-based diatom identification, making it a promising and versatile alternative or extension to traditional light microscopic diatom analyses in the future.

Spectrometers have a wide range of applications ranging from optical to non-optical spectroscopy. The need for compact, portable, and user-friendly spectrometers has been the pivot of attention from small laboratories to the industrial scale. Here, the Czerny Turner configuration-based optical spectrometer simulation design is carried out using ZEMAX OpticStudio. A compact and low-cost optical spectrometer in the visible range has been developed by using diffraction grating as a dispersive element and USB-type WebCAM CCD (Charge-coupled device) as a detector instead of an expensive commercial diffraction grating and detector. Using National Instruments LabVIEW, data acquisition, processing, and displaying techniques are made possible. We have employed different virtual images in LabVIEW programs to collect the pixel-to-pixel information and wavelength-intensity information from the image captured using the WebCAM CCD. Finally, we have shown that the OpticStudio-based spectrometer and experimental measurements of the developed spectrometer are in good agreement.

This paper presents a robust method based on graph topology to find the topologically correct and consistent subset of inter-robot relative pose measurements for multi-robot map fusion. However, the absence of good prior gives a severe challenge to distinguish the inliers and outliers, and wrong loop closures can seriously corrupt the fused global map. Existing works mainly rely on the consistency of spatial dimension to select inter-robot measurements, which does not always hold. In this paper, we propose a fast inter-robot loop closure selection method that integrates the consistency and topology relationship of measurements, which both conform to the continuity characteristics of similar scenes and spatiotemporal consistency. The traditional high-dimensional consistency matrix is decomposed into the sub-matrix blocks corresponding to the overlapping trajectory area. Building on this logic, a clustering method involving topology correctness of inter-robot loop closures is introduced to split the entire measurement set into multiple clusters. We define the weight function to find the maximum cardinality subset with topologically correct and consistent, then convert the weight function to a maximum clique problem in the graph and solve it. We evaluate the performance of our method in a simulation and in a real-world experiment. Compared to state-of-the-art methods, the results show that our method can achieve competitive performance in accuracy while reducing computation time by 75%.

Boosted by the progress in deep learning, Single Image Super-Resolution (SISR) has gained a lot of interest in the Remote Sensing community, who sees it as an oportunity to compensate for satellite's ever-limited spatial resolution with respect to end users needs. While there has been a great amount of work on network architures in the latest years, deep learning based SISR in remote sensing is still limited by the availability of the large training sets it requires. The lack of publicly available large datasets with the required variability in terms of landscapes and seasons pushes researchers to simulate their own dataset by means of downsampling. This may impair the applicability of the trained model on real world data at the target input resolution. In this paper, we propose an open-data licenced dataset composed of 10m and 20m cloud-free surface reflectance patches from Sentinel-2, with their reference spatially-registered surface reflectance patches at 5 meter resolution acquired on the same day by the VENµS satellite. This dataset covers 29 locations on earth with a total of 132 955 patches of 256x256 pixels at 5 meters resolution, and can be used for the training of super-resolution algorithms to bring the spatial resolution of 8 of the Sentinel-2 bands down to 5 meters.

Brain tissue segmentation is an important component of clinical diagnosis of brain diseases by means of multi-modal magnetic resonance imaging (MR). Brain tissue segmentation is developed by many unsupervised methods in literature. The most commonly used unsupervised methods are: K-Means, Expectation Maximization and Fuzzy Clustering. Fuzzy clustering methods offer considerable benefits compared with the aforementioned methods as they are capable of handling brain images which are complex, largely uncertain and imprecise in nature. However, this approach suffers from the intrinsic noise and intensity inhomogeneity (IIH) in the data resulted from the acquisition process. To resolve these issues, we propose a fuzzy consensus clustering algorithm that defines a membership function resulted from a voting schema to cluster the pixels. In particular, we first pre-process the MRI data and employ several segmentation techniques based on traditional fuzzy sets and intuitionistic sets. Then, we adopted a voting schema to fuse the results of the applied clustering methods. Finally, to evaluate the proposed method, we used the well-known performance measures (boundary measure, overlap measure and volume measure) on two publicly available datasets (OASIS and IBSR18). The experimental results show the superior performance of the proposed method in comparison with the recent state of the arts.

Vegetation status assessment is crucial for agricultural monitoring and management. Vegetation indices derived from high resolution image time series can be used to derive key phenological parameters for annual crops. In this work, we propose a procedure for the estimation of these parameters and their associated uncertainties. The approach uses Bayesian inference through Markov Chain Monte Carlo in order to obtain the full joint posterior distribution of the phenological parameters given the satellite observations. The proposed algorithm is quantitatively validated on synthetic data. Its use on real data is presented together with an application to real-time within season estimation allowing for phenology forecasting.

A convolutional neural network (CNN) is sometimes understood as a black box in the sense that while it can approximate any function, studying its structure will not give us any insights into the nature of the function being approximated. In other terms, the discriminative ability does not reveal much about the latent representation of a network. This research aims to establish a framework for interpreting the CNNs by profiling them in terms of interpretable visual concepts and verifying them by means of Integrated Gradient. We also ask the question, "Do different input classes have a relationship or are they unrelated?" For instance, could there be an overlapping set of highly active neurons to identify different classes? Could there be a set of neurons that are useful for one input class whereas misleading for a different one? Intuition answers these questions positively, implying the existence of a structured set of neurons inclined to a particular class. Knowing this structure has significant values; it provides a principled way for identifying redundancies across the classes. Here the interpretability profiling has been done by evaluating the correspondence between individual hidden neurons and a set of human-understandable visual semantic concepts. We also propose an integrated gradient-based class-specific relevance mapping approach that takes the spatial position of the region of interest in the input image. Our relevance score verifies the interpretability scores in terms of neurons tuned to a particular concept/class. Further, we perform network ablation and measure the performance of the network based on our approach.

The use of convolutional neural networks (CNNs) in image classification has become the standard method of approaching computer vision problems. Here we apply pre-trained networks to classify images of non-breaking, plunging and spilling breaking waves. The CNNs are used as basic feature extractors and a classifier is then trained on top of these networks. The dynamic nature of breaking waves is exploited by using image sequences to gain extra information and improve the classification results. We also see improved classification performance in using pre-computed image features such as the optical flow between image pairs. The inclusion of the dynamic information improves the classification between breaking wave classes. We also provide corrections to the methodology from the article from which the data originates to achieve a more accurate assessment of performance.

With the recent coronavirus pandemic (Covid-19) online sales of clothing retail have become a necessity to survive in the market. It is for this reason that the objective of the research was to know the purchase intention of clothing by young consumers through the online channel of small and micro companies (MiPyMe) located in the countries of Mexico, Colombia and Peru. To do this, using a structural equations model, it was intended to analyze the relationships between the variables trust, satisfaction, perceived value, quality of interaction and image in the electronic context as antecedent variables of online purchase intention, in accordance with the theoretical framework. 638 surveys were applied to young consumers in Mexico, Colombia and Peru. Sociodemographic characteristics included gender, age, country, and knowledge of a MiPyMe online clothing store. The results reveal that, of the total of young people surveyed, 62.7% stated that they did not know of any online clothing store of a MiPyMe. The contrasting of the hypotheses indicates that the quality of interaction directly and positively influences perceived value, that the image directly and positively influences the satisfaction and purchase intention of the consumer of the MiPyMe of Colombia, Mexico and Peru. These results may be useful for entrepreneurs in this business sector to rethink their online business strategies, as well as for the governments of these nations to promote this sector with training and specialized technical assistance.

Background: Zebrafish (Danio rerio) is a model organism emerged for the study of human cancer. Compared with the murine model, the zebrafish model has several properties ideal for personalized therapies. The transparency of the zebrafish embryos and the development of the pigment-deficient “casper” zebrafish line give the capacity to directly observe cancer formation and progression in the living animal. Automatic quantification of cellular proliferation in vivo is critical to the development of personalized medicine. Methods: A new methodology was defined to automatically quantify the cancer cellular evolution. ZFTool was developed to establish a base threshold that eliminates the embryo auto-fluorescence and automatically measures the area and intensity of GFP (green-fluorescent protein) marked cells and define a proliferation index. Results: Proliferation index automatically computed on different targets demonstrates the efficiency of ZFTool to provide a good automatic quantification of cancer cell evolution and dissemination. Conclusion: Our results demonstrate that ZFTool is a reliable tool for the automatic quantification of the proliferation index, being a measure of cancer mass evolution in zebrafish eliminating the influence of its autofluorescence.

Apoptosis is associated with numerous phenotypical characteristics, and is thus studied with many tools. In this study, we compared two broadly used apoptotic assays: TUNEL and staining with an antibody targeting the activated form of an effector caspase. To compare them, we developed a protocol based on commonly used tools such as filters, zprojection and thresholding. Even though it is commonly used in imageprocessing protocols, thresholding remains a recurring problem. Here we analyzed the impact of processing parameters and readout choice on the accuracy of apoptotic signal quantification. Our results show that TUNEL is quite robust, even if image processing parameters can allow or not to detect subtle differences of the apoptotic rate. On the contrary, images from anticleaved caspase staining are more sensitive to handle and proved to necessitate to be processed more carefully. We then developed an open source Fiji macro automatizing most steps of the image processing and quantification protocol. It is noteworthy that the field of application of this macro is wider than apoptosis as it can perfectly be used to treat and quantify other kind of images.

The present work proposed a low-cost portable device as an enabling technology for Smart Farms using multispectral imaging and Machine Learning in soil texture. Clay is an important factor for the verification and monitoring of soil use due to its fast reaction to chemical and surface changes. The system developed uses the analysis of reflectance in wavebands for clay prediction. The selection of each wavelength is performed through an LED lamp panel. A NoIR microcamera controlled by a Raspberry Pi device is employed to acquire the image and unfold it in RGB histograms. Results showed an good prediction performance with R2 of 0.96, RMSEC of 3.66% and RMSECV of 16.87%. The high portability allows the equipment to be used in a field providing strategic information related to soil sciences.

Recent neuroscience research results show that the nerve information in the brain is not only encoded by the spatial information. Spiking neural network based on pulse frequency coding plays a very important role in dealing with the problem of brain signal, especially complicated space-time information. In this paper, an unsupervised learning algorithm for bilayer feedforward spiking neural networks based on spike-timing dependent plasticity (STDP) competitiveness is proposed and applied to SAR image classification on MSTAR for the first time. The SNN learns autonomously from the input value without any labeled signal and the overall classification accuracy of SAR targets reached 80.8%. The experimental results show that the algorithm adopts the synaptic neurons and network structure with stronger biological rationality, and has the ability to classify targets on SAR image. Meanwhile, the feature map extraction ability of neurons is visualized by the generative property of SNN, which is a beneficial attempt to apply the brain-like neural network into SAR image interpretation.

This article described the technology of determining earthquake epicenter with radar remote sensing on the example of Sentinel-1A/B. To determine the epicenter of the earthquake, the Earth's crust displacements were analyzed using radar remote sensing data obtained for the ascending and descending flight orbits. Coordinates of Earthquake epicenters were found according to line-of-sight displacement images via its maximum value. Displacement of the Earth's crust was obtained by processing in the GMTSAR package in the VirtualBox virtual machine of the Linux Ubuntu 16.04 operation system. Two earthquakes that occurred in 2020 were studied to determine the accuracy of finding epicenters using the ascending and descending orbits Sentinel-1A/B. These earthquakes occurred in Western Xizang, China, and Doganyol, Turkey. The maximum deviation from the officially registered epicenter coordinates was 15.38 km for Doganyol and 3.2 km for the Western Xizang earthquake. The negative displacement was 90 mm for Doganyol and 50 mm for Western Xizang.

The color and NIR spectrum are key to build an oil estimation model, thus it requires individual olives clustering before the Sohlext oil extraction method can be applied. The objective was to analyze an OC estimation model of individual olives, based on cluster of similar color and NIR spectrum in different combination of the first and/or the second season. This study was performed with Chilean Arbequina olives in 2016 and 2017. The descriptor of the cluster consisted of the 3 color channels of c1, c2, c3 color model plus 11 reflectance points between 1710 and 1735 nm of each olive, normalized with the Z-score index. Clusters of similar color and NIR spectrum were formed with the k-means++ algorithm, leaving a sufficient amount of olives to be able to perform the Sohlext analysis of OC, as reference value. The estimation models were based on the Support Vector Machine. The test was carried out with the Leave One-Out Cross Validation in different training-testing combinations. The best model predicted the OC with 6% and 13%deviation respect to the real value in one season by itself and when one season tested with another season, respectively. The use of clustering in estimation model is discussed.

In the present, the growth of digitization and worldwide communications make OCR systems of exotic languages a very important task. In this paper, we attempt to develop an OCR system for one of these exotic languages with a unique script, Amharic. Motivated by the recent success of the Attention mechanism in Neural Machine Translation (NMT), we extend the attention mechanism for Amharic text-image recognition. The proposed model consists of CNNs and attention embedded recurrent encoder-decoder networks that are integrated following the configuration of the seq2seq framework. The attention network parameters are trained in an end-to-end fashion and the context vector is injected, with the previously predicted output, at each time steps of decoding. Unlike the existing OCR model that minimizes the CTC objective function, the new model minimizes the categorical cross-entropy loss. The performance of the proposed attention-based model is evaluated against the test dataset from the ADOCR database which consists of both printed and synthetically generated Amharic text-line images and achieved promising results with a CER of 1.54% and 1.17% respectively.

Precision agriculture is a technology used by farmers to help food sustainability amidst growing population. One of the tools of precision agriculture is yield monitoring, which helps a farmer manage his production. Yield monitoring is usually done during harvest, however it could also be done early in the growing season. Early prediction of yield, specifically for fruit trees, aids the farmer in the marketing of their product and assists in managing production logistics such as labor requirement and storage needs. In this study, a machine vision system is developed to estimate fruit yield early in the season. The machine vision system uses a color camera to capture images of fruit trees during the full bloom period. An image segmentation algorithm based on an artificial neural network was developed to recognize and count the blossoms on the tree. The artificial neural network segmentation algorithm uses color information and position as input. The resulting correlation between the blossom count and the actual number of fruits on the tree shows the potential of this method to be used for early prediction of fruit yield.

This article presents a new method of segmenting images with gray levels. The method is based on determining several thresholds for separation of gray levels. The determination of these thresholds is done using the certainty of the neutrosophic information. The concept of this method can be stated simply: to choose the local maximums for the neutrosophic certainty.

3D-mapping-aided (3DMA) global navigation satellite system (GNSS) positioning that improves positioning performance in dense urban areas has been under development in the recent years, but it still faces many challenges. This paper details a new algorithm that explores the potential of using building boundary for positioning. Rather than applying complex simulations to analyze and correct signal reflections by buildings, the approach utilizes a convolutional neural network to differentiate between the sky and building in a sky-pointing fisheye image. A new skymask matching algorithm is then proposed to match the segmented fisheye images with skymasks generated from a 3D building model. Each matched skymask holds a latitude and longitude coordinate to determine the precise location of the fisheye image. The results are then compared with the conventional GNSS and advanced 3DMA GNSS positioning methods. The aims of the proposed algorithm are to increase positioning and heading accuracy in a rich urban environment.

Nutrient estimation in forest ecosystems through satellite images allows us to obtain accurate data, starting with data transformation from forest stands and the existing relationship with the spectral information of the image through modeling. The objective of the study was to quantify and validate the content of C, N, H in aboveground tree biomass in managed stands using spatial modeling and satellite images. This study was conducted during 2017-2018 in managed forest stands in San Juan Lachao, Oaxaca, Mexico. Fifteen 400 m2 experimental sites were selectively established, using a completely randomized experimental design of five silvicultural treatments with three replications. As part of data preprocessing, normality and homogeneity of variances assumptions were checked using the Shapiro-Wilk and Bartlett tests, respectively. From the pixels, data of the average of Normalized Difference Vegetation Index (NDVI) that surrounded the sampling sites were contrasted against the data obtained from forest inventory and the regression models to estimate C, N, H and biomass were generated. Models were validated by NDVI. With the models we estimated 0.95 t ha-1 biomass, which contains between 0.61 and 0.63 of C, 0.44-0.46 of N and 0.24 of H. The models generated had coefficients of determination (R2) of 0.85 to 0.87, which are significant parameters (p ≤ 0.0001). These results confirm that the use of Sentinel satellite images in the estimation of these elements in forest ecosystems based on the relationship between data inventory and the NDVI is highly reliable.

In order to realize rapid and nondestructive monitoring of wheat biomass in field, field experiments based on different densities, nitrogen fertilizer and variety treatments were studied. RGB images of wheat in the main growth stage were obtained by UAV, and wheat color and texture feature indices were obtained by image processing, and wheat biomass was obtained by field sampling in the same period. Then the relationship between different color and texture feature indices and wheat biomass was analyzed to select the color and texture feature index suitable for wheat biomass estimation. The results showed that there was a high correlation between image color index and wheat biomass in different stages, and most of them reached a very significant correlation level. However, the correlation between image texture feature index and wheat biomass was poor, only a few indexes reached significant or extremely significant correlation level. Based on the above results, the color indices with the highest correlation to wheat biomass or the combining indices of color and texture feature in different growth stage were used to construct estimation model of wheat biomass. The models were validated using independently measured biomass data, and the correlation between simulated and measured values reached the significant level, RMSE were smaller. This indicated that the estimated results by the models were reliable and accurate. It also showed that the estimation models of wheat biomass combined with color and texture feature indices of UAV image were better than the single color index models. The results would provide a new method for real-time monitoring of wheat field growth and biomass estimation.

The latest methods based on deep learning have achieved amazing results regarding the complex work of inpainting large missing areas in an image. This type of method generally attempts to generate one single "optimal" inpainting result, ignoring many other plausible results. However, considering the uncertainty of the inpainting task, one sole result can hardly be regarded as a desired regeneration of the missing area. In view of this weakness, which is related to the design of the previous algorithms, we propose a novel deep generative model equipped with a brand new style extractor which can extract the style noise (a latent vector) from the ground truth image. Once obtained, the extracted style noise and the ground truth image are both input into the generator. We also craft a consistency loss that guides the generated image to approximate the ground truth. Meanwhile, the same extractor captures the style noise from the generated image, which is forced to approach the input noise according to the consistency loss. After iterations, our generator is able to learn the styles corresponding to multiple sets of noise. The proposed model can generate a (sufficiently large) number of inpainting results consistent with the context semantics of the image. Moreover, we check the effectiveness of our model on three databases, i.e., CelebA, Agricultural Disease, and MauFlex. Compared to state-of-the-art inpainting methods, this model is able to offer desirable inpainting results with both a better quality and higher diversity. The code and model will be made available on https://github.com/vivitsai/SEGAN.

The objective to fast-track the mapping and registration of large numbers of unrecorded land rights globally, leads to the experimental application of Artificial Intelligence (AI) in the domain of land administration, and specifically the application of automated visual cognition techniques for cadastral mapping tasks. In this research, we applied and compared the ability of rule-based systems within Object Based Image Analysis (OBIA), as opposed to human analysis, to extract visible cadastral boundaries from Very high resolution (VHR) World View-2 image, in both rural and urban settings. From our experiments, machine-based techniques were able to automatically delineate a good proportion of rural parcels with explicit polygons where the correctness of the automatically extracted boundaries was 47.4% against 74.24% for humans and the completeness of 45% for machine, as against 70.4% for humans. On the contrary, in the urban area, automatic results were counterintuitive: even though urban plots and buildings are clearly marked with visible features such as fences, roads and tacitly perceptible to eyes, automation resulted in geometrically and topologically poorly structured data, that could neither be geometrically compared with human digitised, nor actual cadastral data from the field. These results provide an updated snapshot with regards to the performance of contemporary machine-drive feature extraction techniques compared to conventional manual digitising.

In the fashion field, the use of electroplated small metal parts such as studs, clips and buckles is widespread. The plate is often made of precious metal, such as gold or platinum. Due to the high cost of these materials, it is strategically relevant and of primary importance for manufacturers to avoid any waste by depositing only the strictly necessary amount of material. To this aim, Companies need to be aware of the overall number of items to be electroplated so that it is possible to properly set the parameters driving the galvanic process. Accordingly, the present paper describes a Machine Vision-based method able to automatically count small metal parts arranged on a galvanic frame. The devised method relies on the definition of a proper acquisition system and on the development of image processing-based routines. Such a system is then implemented on a counting machine is meant to be adopted in the galvanic industrial practice to properly define a suitable set or working parameters (such as current, voltage and deposition time) for the electroplating machine and, thereby, to assure the desired plate thickness from one side and to avoid material waste on the other.

Spike shape and morphometric characteristics are among the key characteristics of cultivated cereals associated with their productivity. Identification of the genes controlling these traits requires morphometric data harvesting and analysis for numerous plants, which is automatable using technologies of digital image analysis. A method for wheat spike morphometry utilizing 2D image analysis is proposed. Digital images are acquired in two variants: a spike on a table (one projection) or fixed with a clip (four projections). The method identifies spike and awns in the image and estimates their quantitative characteristics (area in image, length, width, circularity, etc.). Models of sections, quadrilaterals, and radial model are proposed for describing spike shape. Parameters of these models are used to predict spike shape type (spelt, normal, or compact) by machine learning. The mean error in spike density prediction for the images in one projection is 4.61 (~18%) versus 3.33 (~13%) for the parameters obtained using four projections. F1 measure in automated spike classification into three types is 0.78 using logistic regression (one projection) and 0.85 using random forest method (four projections). The proposed method is implemented in Java; examples of images and user guide are available at http://wheatdb.org/werecognizer.

A lensless camera is an ultra-thin computational-imaging system. Existing lensless cameras are based on the axial arrangement of an image sensor and a coding mask, and therefore, the back side of the image sensor cannot be captured. In this paper, we propose a lensless camera with a novel design that can capture the front and back sides simultaneously. The proposed camera is composed of multiple coded image sensors, which are complementary-metal-oxide-semiconductor~(CMOS) image sensors in which air holes are randomly made at some pixels by drilling processing. When the sensors are placed facing each other, the object-side sensor works as a coding mask and the other works as a sparsified image sensor. The captured image is a sparse coded image, which can be decoded computationally by using compressive-sensing-based image reconstruction. We verified the feasibility of the proposed lensless camera by simulations and experiments. The proposed thin lensless camera realizes super field-of-view imaging without lenses or coding masks, and therefore can be used for rich information sensing in confined spaces. This work also suggests a new direction in the design of CMOS image sensors in the era of computational imaging.

Thermal infrared imagery provides temperature information on target objects, and has been widely applied in non-destructive testing. However, thermal infrared imagery is not always able to display detailed textures of inspected objects, which hampers the understanding of geometric entities consisting of temperature information. Although some commercial software has been developed for 3D thermal model displays, the software requires the use of expensive specific thermal infrared sensors. This study proposes a cost-effective method for 3D thermal model reconstruction based on image-based modeling. Two smart phones and a low-cost thermal infrared camera are employed to acquire visible images and thermal images, respectively, that are fused for 3D thermal model reconstruction. The experiment results demonstrate that the proposed method is able to effectively reconstruct a 3D thermal model which extremely approximates its corresponding entity. The total computation time for the 3D thermal model reconstruction is intensive while generating dense points required for the creation of a geometric entity. Future work will improve the efficiency of the proposed method in order to expand its potential applications to in-time monitoring.

Global food security for the increasing world population not only requires increased sustainable production of food but a significant reduction in pre- and post-harvest waste. The timing of when a fruit is harvested is critical for reducing waste along the supply chain and increasing fruit quality for consumers. The early in field assessment of fruit ripeness and prediction of the harvest date and yield by non-destructive technologies have the potential to revolutionize farming practices and enable the consumer to eat the tastiest and freshest fruit possible. A variety of non-destructive techniques have been applied to estimate the ripeness or maturity but not all of them are applicable for in situ (field or glasshouse) assessment. This review focuses on the non-destructive methods which are promising, or have already been, applied to the pre-harvest in field measurement including colorimetry, visible imaging, spectroscopy and spectroscopic imaging. Machine learning and regression models used in assessing ripeness are also discussed.

Sports video analysis is an interesting and emerging field in the domain of computer vision. It has become a very popular subject among scholars all over the world in the last decade due to the need of automated classification of events such as offsides, in/out situations, goals and so on. The most important problem in sports video analysis is detection and tracking of ball and players. Extraction of court lines is also another important problem since it is necessary in the classification of aforementioned events. In this study, considering some of the previous work on the subject, we develop a method for tracking the ball and players in tennis videos where the camera position and orientation is supposed fixed.

Edge detection is one of the key issues in the field of computer vision and remote sensing image analysis. Although many different edge-detection methods have been proposed for gray-scale, color, and multispectral images, they still face difficulties when extracting edge features from hyperspectral images (HSIs) that contain a large number of bands with very narrow gap in the spectral domain. Inspired by the clustering characteristic of the gravitation, a novel edge-detection algorithm for HSIs is presented in this paper. In the proposed method, we first construct a joint feature space by combining the spatial and spectral features. Each pixel of HSI is assumed to be a celestial object in the joint feature space, which exerts gravitational force to each of its neighboring pixel. Accordingly, each object travels in the joint feature space until it reaches a stable equilibrium. At the equilibrium, the image is smoothed and the edges are enhanced, where the edge pixels can be easily distinguished by calculating the gravitational potential energy. The proposed edge-detection method is tested on several benchmark HSIs and the obtained results were compared with those of three state-of-the-art approaches. The experimental results confirm the efficacy of the proposed method

Saliency detection in synthetic aperture radar (SAR) image is a difficult problem. This paper proposed a multitask saliency detection (MSD) model for the saliency detection task of SAR image. Firstly, we extract four features of SAR image as the input of the MSD model, which include the intensity, orientation, uniqueness and global contrast. Then, the saliency map is generated by the multitask sparsity pursuit (MTSP) which integrates the multiple features collaboratively. Subjective and objective evaluation of the MSD model verifies its effectiveness. Based on the saliency maps of the source images, an image fusion method is proposed for the SAR and color optical image fusion. The experimental results of real data show the proposed image fusion method is superior to the presenting methods in terms of several universal quality evaluation indexes, as well as in the visual quality. The salient areas in the SAR image can be highlighted and the spatial and spectral details of color optical image can also be preserved in the fusion result.

Recent developments in microfluidics and biomaterials have enabled the creation of organs-on-chips (OoCs), which provide a controlled and lifelike in vitro microenvironment resembling an actual organ. Organ-on-a-chip technology offers a tremendous opportunity for efficient, cost-effective, and ethical drug testing and research. However, the high throughput and large quantities of data these complex microenvironments produce make it difficult for researchers to effectively analyze the data and draw valuable conclusions. Convolutional Neural Networks (CNNs) are most aptly positioned to address many of OoC’s challenges because of their ability to interpret microscopic images and facilitate the analytical process. Despite the growing field of AI, there have been a limited number of studies summarizing the various applications of CNNs in OoCs. This review aims to provide 1) an overview of the technology involved with CNNs and OoCs 2) an insight into the state-of-the-art applications of CNNs in OoCs including device parameters, predicting and tracking cell trajectories, super-resolution image segmentation, and image classification, and 3) an overview of existing challenges and opportunities ahead for clinical translation of this technology. Various applications of CNNs have been classified by the type of task. Different CNN models such as Faster R-CNNs, fully convolutional networks (FCNs), and Mask R-CNNs are explained and highlighted. This review article can be used as a resource for a better understanding of the potential of CNNs in biomedical research and clinical applications, particularly OoCs.

: Image guided superficial radiation therapy (IGSRT) combines superficial radiation therapy (SRT) with full dermal visualization (FDV) via high resolution dermal ultrasound (HRDUS) for the treatment of non-melanoma skin cancer (NMSC). The gold standard for IGSRT delivery includes a comprehensive cancer care model with support for the dermatologists by a multidisciplinary team. When delivered in this model, IGSRT can achieve cure rates of 99% for treatment of NMSC. This paper focuses on the benefits of IGSRT during therapy. Medical records from 7 dermatology clinics of 883 patients with 1,507 cases of NMSC treated with IGSRT be-tween 2017 and 2018 were retrospectively reviewed. 92% of the NMSC lesions showed daily depth fluctuations, 60.32% of lesions did not require changes during therapy, and nearly 40% of lesions required at least one compensatory change during therapy. 83% of NMSC lesions were labeled high-risk based on the 2024 NCCN guidelines. Increasing and decreasing tumor depth measurements during IGSRT inform dermatologists when adaptive changes in energy (kV), TDF and dose will result in more efficacy and less toxicity, respectively.

In recent years, demand has been increasing for target detection and tracking from aerial imagery via drones using onboard powered sensors and devices. We propose a very effective method for this application based on a deep learning framework. A state-of-art embedded hardware system empowers small flying robots to carry out the real-time onboard computation necessary for object tracking. Two types of embedded modules were developed: one is designed using a Jetson TX or AGX Xavier, and the other is based on an Intel Neural Compute Stick. These are suitable for real-time onboard computing power on small flying drones with limited space. A comparative analysis of current state-of-art deep-learning-based multi-object detection algorithms was carried out utilizing the designated GPU-based embedded computing modules to obtain detailed metric data about frame rates as well as the computation power. We also introduce an effective target tracking approach for moving objects. The algorithm for tracking moving objects is based on the extension of simple online and real-time tracking. It was developed by integrating a deep-learning-based association metric approach (Deep SORT), which uses a hypothesis tracking methodology with Kalman filtering and a deep-learning-based association metric. In addition, a guidance system that tracks the target position using a GPU-based algorithm is introduced. Finally, we demonstrate the effectiveness of the proposed algorithms by real-time experiments with a small multi-rotor drone.

The study of hepatitis C virus (HCV) replication in cell culture is mainly based on cloned viral isolates requiring adaptation for efficient replication in Huh7 hepatoma cells. The analysis of wild type (WT) isolates has been enabled by expression of SEC14L2 and by inhibitors targeting deleterious host factors. Here, we aimed at optimizing cell culture models to allow infection with HCV from patient sera. We used Huh7-Lunet cells ectopically expressing SEC14L2, CD81 and a GFP reporter with nuclear translocation upon cleavage by the HCV protease to study HCV replication, combined with a drug-based regimen for stimulation of non-modified wildtype isolates. RT-qPCR based quantification of HCV infections using patient sera suffered from high background in the daclatasvir treated controls. We therefore established an automated image analysis pipeline based on imaging of whole wells and iterative training of a machine-learning tool, using nuclear GFP localization as a readout for HCV infection. Upon visual validation of hits assigned by the automated image analysis the method revealed no background in daclatasvir treated samples. Thereby, infection events were found for 15 of 34 high titer HCV genotype (gt) 1b sera, revealing a significant correlation of serum titer and successful infection. We further show that transfection of viral RNA extracted from sera can be used in this model as well, albeit with so far limited efficiency. Overall, we generated a robust serum infection assay for gt1b isolates using semi-automated image analysis, which was superior to conventional RT-qPCR based quantification of viral genomes.

This article reports on the development of a big data processing platform that uses hot words（politics, economy, military, culture and society） reflecting the national image as the search object and the key time points as the time domain of data resources. The platform crawls (in real time), continuously expands and dynamically monitors a corpus of mainstream media covering China in developed countries (including the United States, Britain, Germany, France and Japan) and in developing countries (such as Brazil, India and South Africa). In this study, the principles of platform development and operation and the software and hardware operating environment were determined.

Bone fractures are common injuries that require quick and accurate diagnosis to provide the right medical care. The “convolutional neural network” technique relies mostly on manual inspection, by radiologists and can be laborious and prone to error. The objective of this work was to increase the efficiency and accuracy of fracture identification by automating the examination of X-ray images using convolutional neural networks. A “convolutional neural network” model created to detect bone fractures in X-ray images was used in the present study. The model was trained and validated using an extensive dataset of X-ray images, which included both fractured and nonfractured bones. Multiple convolutional layers were used in the “convolutional neural network” architecture for feature extraction, and pooling and fully connected layers were added for classification. The main measures used to assess the model's performance were sensitivity, specificity, and accuracy. In regard to identifying bone fractures, the “convolutional neural network”-based model outperformed the conventional technique. With significant gains in sensitivity and specificity, this approach achieved a high accuracy rate and decreased the frequency of false positives and false negatives. We used “convolutional neural network” tool in PyTorch for bone fracture detection and we outlined the important considerations that must be considered when attempting to achieve this goal Additionally, we contrasted every study with our baseline. Here our maximum accuracies were 99.05%, 98.60%, 99.45% and 99.74% for epochs 8, 9, 10 and 11, respectively. These findings highlight the model's ability to improve the diagnostic efficiency and accuracy in clinical settings. Using “convolutional neural network” to identify bone fractures from X-ray images is a promising development in medical imaging. In the end, our method improves patient outcomes by ensuring faster and more reliable fracture diagnosis while also lessening the diagnostic burden on radiologists. Subsequent investigations will focus on incorporating this system into clinical procedures and investigating its utilization in real-time emergency situations. Since there was no medical intervention on human subjects in this investigation, trial registration regulations were not applied.

Background/Objectives: This study investigates the relationship between diet quality and body image disturbance among young women aged 18-24, a crucial period for establishing lifelong health behaviors. Given the increasing exposure to social media, which often promotes unrealistic beauty standards, this research aims to explore how eating behaviors and diet quality, correlates with body image disturbance; Methods: A mixed-methods approach was employed, combining qualitative focus group discussions with quantitative analysis. Focus groups (n=19) explored themes of body image dissatisfaction. The Body Image Disturbance Questionnaire (BIDQ) was administered to 50 participants (young women aged 18-24) to quantitatively assess body image disturbance, while diet quality was evaluated using the Australian Recommended Food Scores (ARFS). The Three-Factor Eating Questionnaire (TFEQ-R18) was also used to assess eating be-haviors, including cognitive restraint, uncontrolled eating, and emotional eating. A social in-fluence questionnaire (SIQ) was administered to measure the effect of social influence. Pearson's correlation coefficient was used to determine the relationship between ARFS, BIDQ, and TFEQ-R18 scores; Results: Qualitative findings revealed persistent dissatisfaction with body shape, largely influenced by social media. Quantitatively, 65% of participants scored above the clinical threshold for body image disturbance (mean BIDQ score = 4.2, SD = 0.8). The correlation between ARFS scores and BIDQ scores was weak and not statistically significant (r = 0.057, p = 0.711). However, a sig-nificant positive correlation was found between time spent on social media and body image disturbance (r = 0.58, p < 0.01). Additionally, TFEQ-R18 results indicated that 45% of participants displayed moderate levels of uncontrolled eating, and 36.5% demonstrated moderate levels of emotional eating; Conclusions: While social media significantly influences body image concerns, its effect on eating behaviours and diet quality shows weak correlations, suggesting that other factors may mediate these outcomes. These results highlight the complexity of the relationship between body image, eating behaviors, and diet quality, indicating that interventions should address the psychological drivers behind these concerns, rather than solely focusing on reducing social media exposure.

Due to the inherent limitations of a single viewpoint, reconstructing 3D human meshes 1 from a single image has long been a challenging task. While deep learning networks enable us to 2 approximate the shape of unseen sides, capturing the texture details of the non-visible side remains 3 difficult with just one image. Traditional methods utilize Generative Adversarial Networks (GANs) 4 to predict the normal maps of the non-visible side, thereby inferring detailed textures and wrinkles 5 on the model’s surface. However, we have identified challenges with existing normal prediction 6 networks when dealing with complex scenes, such as a lack of focus on local features and insufficient 7 modeling of spatial relationships.To address these challenges, we introduce EMAR—Enhanced 8 Multi-scale Attention-driven Single Image 3D Human Reconstruction. This approach incorporates 9 a novel Enhanced Multi-Scale Attention (EMSA) mechanism, which excels at capturing intricate 10 features and global relationships in complex scenes. EMSA surpasses traditional single-scale attention 11 mechanisms by adaptively adjusting the weights between features, enabling the network to more 12 effectively leverage information across various scales.Furthermore, we have improved the feature 13 fusion method to better integrate representations from different scales. This enhanced feature fusion 14 allows the network to more comprehensively understand both fine details and global structures 15 within the image. Finally, we have designed a hybrid loss function tailored to the introduced attention 16 mechanism and feature fusion method, optimizing the network’s training process and enhancing the 17 quality of reconstruction results.Our network demonstrates significant improvements in performance 18 for 3D human model reconstruction. Experimental results show that our method exhibits greater 19 robustness to challenging poses compared to traditional single-scale approaches.

Coffee is one of the most valuable agricultural products worldwide, and it is crucial to have efficient tools to obtain reliable information about production. This study aims to estimate coffee plantation production using segmentation and deep learning techniques in RGB images. Photographs of coffee plants were taken in Tabaconas, San Ignacio-Cajamarca, to create a dataset of crops during the harvest stage. The images were segmented to detect coffee fruits. A deep learning method was developed with YOLOv5 to detect the fruits and OpenCV to count them. The results showed that YOLOv5 achieved an accuracy of 97.25%, a recall of 95.77%, and an F1-Score of 96.37%, demonstrating high reliability in detecting coffee fruits. The average detection time per image was 17.9 seconds. The metrics were evaluated using a confusion matrix, highlighting the model's good performance. In conclusion, segmentation and deep learning techniques, along with counting algorithms developed with OpenCV, proved effective for estimating coffee production. This approach provides a valuable tool for farmers, improving crop management and facilitating decision-making in precision agriculture.

Accurate segmentation of skin lesions from dermoscopic images is crucial for early skin cancer detection, yet variations in lesion appearance and image artifacts present challenges. This study proposes two deep learning models, UCM-NetV2 and BNN-UCM-NetV2, to improve accuracy and computational efficiency. UCM-NetV2 enhances the UCM-Net architecture with a novel "cyber-structure" combining Multilayer Perceptron and CNN layers, improving prediction accu-racy without increasing the model's parameter count. BNN-UCM-NetV2 further optimizes for mobile deployment by binarizing activations and weights into 1-bit values, reducing computa-tional complexity and model size. Evaluations on the ISIC2017 and ISIC2018 datasets demonstrate that UCM-NetV2 outperforms existing methods in both accuracy and computational efficiency, achieving this with less than 0.04 GFLOPs. These models have the potential to make skin lesion analysis accessible to a broader population, particularly in resource-limited settings, allowing for proactive skin health monitoring and facilitating teledermatology. To foster further innovation in mobile health diagnostics, the source code for UCM-NetV2 will be available on GitHub: https://github.com/chunyuyuan/UCMV2-Net.

Discovered as an apparent pattern, a universal relation between geometry and information called the holographic principle has yet to be explained. This relation is unfolded in the present paper. As it is demonstrated there, the origin of the holographic principle lies in the fact that a geometry of physical space has only a finite number of points. Furthermore, it is shown that the puzzlement of the holographic principle can be explained by a magnification of grid cells used to discretize geometrical magnitudes such as areas and volumes into sets of points. To wit, when grid cells of the Planck scale are projected from the surface of the observable universe into its interior, they become enlarged. For that reason, the space inside the observable universe is described by the set of points whose cardinality is equal to the number of points that constitute the universe’s surface.

In the field of 2-D image processing and computer vision, accurately detecting and segmenting objects in scenarios where they overlap or are obscured remains a challenge. This difficulty is worse in the analysis of shoeprints used in forensic investigations because they are embedded in noisy environments such as the ground and can be indistinct. Traditional Convolutional Neural Networks (CNNs), despite their success in various image analysis tasks, struggle with accurately delineating overlapping objects due to the complexity of segmenting intertwined textures and boundaries against a background of noise. This study introduces employs the YOLO (You Only Look Once) model enhanced by edge detection and image segmentation techniques to improve the detection of overlapping shoeprints. By focusing on the critical boundary information between shoeprint textures and the ground, our method demonstrates improvements in sensitivity and precision, achieving confidence levels above 85% for minimally overlapped images and maintaining above 70% for extensively overlapped instances. Heatmaps of convolution layers were generated to show how the network converges towards successful detection using these enhancements. This research may provide a potential methodology for addressing the broader challenge of detecting multiple overlapping objects against noisy backgrounds.

Utilizing deep learning for semantic segmentation of cropland from remote sensing imagery has become a crucial technique in land surveys. Cropland illustrates diverse morphologies and degrees of fragmentation on the Earth’s surface, underscoring the importance of accurately perceiving the complex boundaries of cropland which are crucial for effective segmentation. This paper introduces a UNet-like boundary-aware compensation model BAFormer. Cropland boundaries typically exhibit rapid transformations in pixel values and texture features, often appearing as high-frequency features in remote-sensing images. To enhance the recognition of these high-frequency features as represented by cropland boundaries, the proposed BAFormer integrates a Feature Adaptive Mixer (FAM) and develops a Deep Wide Large Kernel Multi-Layer Perceptron (DWLK-MLP) to enrich the global and local cropland boundaries features separately. Specifically, FAM adaptively mixes high-frequency and low-frequency features through the advantages of convolution and self-attention; DWLK-MLP expands the convolutional receptive field by deeply decomposing large kernel convolutions. The efficacy of BAFormer has been evaluated on the Vaihingen, Potsdam, and LoveDA public datasets, as well as the Mapcup dataset. It has demonstrated advanced performance, achieving mIoU scores of 84.5%, 87.3%, 53.5%, and 83.1% on these datasets respectively. Notably, BAFormer-T, the lightweight iteration of the model, surpasses other lightweight models on the Vaihingen dataset with scores of 91.3% F1 and 84.1% mIoU. The source code is available at https://github.com/WangYouM1999/BAFormer.

Target recognition and fine-grained ship classification in remote sensing face the challenges of high inter-class similarity and sample scarcity. A transfer fusion-based ship image harmonization algorithm is proposed to overcome these challenges. This algorithm designs a feature transfer fusion strategy based on the combination of region aware instantiation and attention mechanism. Implement adversarial learning through image harmony generator and discriminator module to generate realistic remote sensing ship harmony images. Furthermore, the do-main encoder and domain discriminator modules are responsible for extracting feature representations of foreground and background, and further aligning ship foreground with remote sensing ocean background features through feature discrimination. Compared with other advanced image conversion techniques, our algorithm delivers more realistic visuals, improving classification accuracy for six ship types by 3% and twelve types by 2.94%, outperforming Sim2RealNet. Finally, a mixed dataset containing data augmentation and harmonizing samples and real data was proposed for the fine-grained classification task of remote sensing ships. Evaluation experiments were conducted on 8 typical fine-grained classification algorithms, and the accuracy of fine-grained classification for all categories of ships was analyzed. The experimental results show that the mixed dataset proposed in this paper effectively alleviates the long tail problem in real datasets, and the proposed remote sensing ship data augmentation framework performs better than state-of-the-art data augmentation methods in fine-grained ship classification tasks.

Background: This study assesses the effects of the ‘Radiant’ image enhancement technique on fetal nuchal translucency (NT) measurements during first trimester sonographic ex-ams. Methods: A retrospective analysis of 263 ultrasound images of first trimester midsagittal sections was conducted. NT measurements were obtained using a semi-automatic tool. Statistical methods were applied to compare NT measurements with and without ‘Radi-ant’ enhancement. An in-vitro setup with predefined line distances provided additional data. Results: Incremental increases in NT measurements were observed with varying levels of ‘Radiant’ application: An average increase of 0.19 mm with ‘Radiant min’, 0.24 mm with ‘Radiant mid’ and 0.30 mm with ‘Radiant max.’ The in-vitro results supported these findings, showing consistent effects on line thickness and measurement accuracy, with the smallest mean deviation occurring at the ‘Radiant mid’ setting. Conclusions: ‘Radiant’ image enhancement leads to significant increases in NT measure-ments. To avoid systematic biases in clinical assessments, it is advisable to disable ‘Radi-ant’ during NT measurement procedures. Further studies are necessary to corroborate these findings and to consider updates to the NT reference tables based on this technology.

Color image binarization plays a pivotal role in image preprocessing work, significantly impacting subsequent tasks, particularly in text recognition. This paper concentrates on Document Image Binarization (DIB), aiming to separate a image into foreground (text) and background (non-text content). Through a thorough analysis of conventional and deep learning-based approaches, we conclude that prevailing DIB methods leverage deep learning technology. Furthermore, we explore the receptive fields pre- and post-network training to underscore the Transformer model's advantages. Subsequently, we introduce a lightweight model based on the U-Net structure, enhanced with the Mobile ViT module to better capture global information features in document images. Given its adeptness at learning both local and global features, our proposed model exhibits superior performance on two standard datasets (DIBCO2012 and DIBCO2017) compared to state-of-the-art methods. Notably, our proposed DIB method presents a straightforward end-to-end model devoid of additional image preprocessing or post-processing. Moreover, its parameter count is less than a quarter of the HIP'23 model, which achives best results on three datasets(DIBCO2012, DIBCO2017 and DIBCO2018). Finally, two sets of ablation experiments were conducted to verify the effectiveness of the proposed binarization model.

In this paper we propose a module that uses features learned by a deep convolutional neural network to infer areas of corrosion and segment pixels to corrosion areas of inspection interest. Our segmentation module is based on eigen tree decomposition and information based decision criteria. To interrogate performance we use several state-of-the-art deep learning architectures and compare to our approach. The results indicate that our method produces better results in terms of accuracy and precision, whilst maintaining good (f-score) significance over the entire dataset.

Deep learning is widely used in image segmentation, effectively identifying crops and weeds and contributing to the reduction of herbicide use. Traditional methods of applying pesticides over large areas are inefficient in dealing with weeds as needed, leading to massive waste of pesticides, high-cost production, and serious environmental pollution. This affects crop yield and quality. Although weed recognition using conventional deep learning methods has evolved over time, there are still challenges in weed extraction, detection, and segmentation. Accurate recognition and detection of weeds are essential prerequisites for implementing variable spraying. This paper proposes a semantic segmentation method based on UNet++ for complex environments where accurate identification of plants and weeds is challenging, targeting weeds in sugar beets, peas, and rice. An attention module is integrated into the upsampling process of UNet++, and UNet++ is used as a backbone network to effectively integrate multi-scale information, efficiently suppressing external noise interference. The UNet++ model with an integrated attention mechanism module achieves higher IOU than the general UNet++ model used in medical image analysis. This method effectively detects crops and weeds in complex backgrounds, providing reference material for the accurate application of robotic herbicides.

Cloud-induced atmospheric extinction and occlusion have a major effect on the effectiveness and quality of telescope observations. Real-time cloud cover distribution and long-term statistical data are essential for astronomical siting and telescope operations. At ground-based astronomical telescope sites, cloud cover distribution is currently analyzed using manual observation methods. However, the main disadvantages of manual observation methods are human subjective, heavy workloads and poor real-time performance. Therefore, a real-time automatic cloud images classification method is desperately needed. This paper presents a new cloud identification method, which is named PSO+XGBoost model by combining eXtreme Gradient Boosting (XGBoost) and Particle Swarm Optimization (PSO). The entire cloud image is divided into 37 sub-regions in order to more precisely identify the distribution of the clouds. 19 features are then extracted, including the sky background, star density, lighting conditions, and subregion grayscale values. The experimental results have shown that the overall classification accuracy of 96.91% and our model is able to outperform several state-of-the-art baseline methods. Our approach achieves high accuracy in comparison with the manual observation methods. Moreover, this method meets telescope real-time scheduling requirements.

Image stitching is a crucial aspect of image processing. However, factors like perspective and environment often lead to irregular shapes in stitched images. Cropping or completion methods typically result in substantial loss of information. This paper proposes a method for rectifying irregularly images into rectangles using deformable meshes and residual networks. The method utilizes a convolutional neural network to quantify rigid structures of images. Choosing the most suitable mesh structure based on the extraction results, offering options such as triangular, rectangular, and hexagonal. Subsequently, the irregularly image, predefined mesh structure, and predicted mesh structure are input into a wide residual neural network for regression. The loss function comprises local and global, aimed at minimizing the loss of image information within the mesh and global target. This method not only significantly reduces information loss during rectification but also adapting to different images with various rigid structures. Validation on the DIR-D dataset shows this method outperforms state-of-the-art methods in image rectification.