There is growing recognition of the adverse effects of body image dissatisfaction (BID) and eating disorder (ED) symptoms on adolescent health. The aim of this study was to estimate the prevalence of ED symptoms, BID, and their relationship in adolescents from public schools in Southern Brazil. A total of 782 schoolchildren (male: n=420, female: n=362); age: 15 ± 0,4 years) answered a self-administrated questionnaire to identify sociodemographic data. Children´s Figure Rating Scale was adopted to identify body image and Eating Attitudes Test (EAT-26) was applied to investigate ED symptoms. Inferential statistics and hierarchical model-controlled logistic regression were used for association between variables. Most of the schoolchildren reported being satisfied with their bodies. However, we observed a higher prevalence of dissatisfaction among girls for being overweight and thinness among boys. Female students and students from schools located in the central area of the city showed higher chances of developing ED symptoms, and the absence of symptoms of ED appeared to act as a protective factor against BID in schoolchildren. Results of this study show the need to reflect on these factors that influence the development of ED and non-acceptance of their own body in a population concerned with their physical appearance.

Autonomous image recognition has numerous potential applications in the field of planetary science and geology. For instance, having the ability to classify images of rocks would allow geologists to have immediate feedback without having to bring back samples to the laboratory. Also, planetary rovers could classify rocks in remote places and even in other planets without needing human intervention. Shu et al. classified 9 different types of rock images using a Support Vector Machine (SVM) with the image features extracted autonomously. Through this method, the authors achieved a test accuracy of 96.71%. In this research, Convolutional Neural Networks(CNN) have been used to classify the same set of rock images. Results show that a 3-layer network obtains an average accuracy of 99.60% across 10 trials on the test set. A version of Self-taught Learning was also implemented to prove the generalizability of the features extracted by the CNN. Finally, one model has been chosen to be deployed on a mobile device to demonstrate practicality and portability. The deployed model achieves a perfect classification accuracy on the test set, while taking only 0.068 seconds to make a prediction, equivalent to about 14 frames per second.

The development of unmanned satellite space technology is increasingly willing, the emergence of medium resolution satellites with sensitivity and spectral variants such as Landsat is very effective in observing environmental changes, while the purpose of this study is to monitor the development of built-in land using image transformation techniques, estimating built-in land changes. The research method uses the NDVI image transformation technique, NDBI and Built Up Index, with Landsat satellite image data obtained from USGS. Accuracy sampling is done by purposive sampling with confusion matrix accuracy test technique. The research results were found. developed land for the period 2004 - 2010 with a percentage of 19.25%, for stages 2010 - 2018 with a percentage of 30.25%. The land development was built based on the area of ​​the highest sub-district in the Kubung area in the early period with a percentage of 7.20% then in the second period with a percentage of 32.23%. The quality of the accuracy of the results of image analysis using confusion matrix technique with an image accuracy level in a field sample of 185 with an image accuracy of 86.04%.

The focus of this study is to identify whether resilience and sustainable development can be used as an image for strategic planning of the city marketing. Resilience is about building and planning for future proof the cities. How urban challenges and crisis have the lowest impact and the maximum of bounce back and evolution. Resilience is part of the sustainable development. Thus, it is important for the decision-makers to define the mission on their strategic planning in a holistically way taking into consideration the basic assets of a city, the environment, the economy and the society and how can all of them can be combined to marketing the city and take into consideration the internal and external environment. As the past few years’ city marketing has become an important tool for the urban development. The main goal is to show how city marketing can be applied on a city that tries to be more resilient and more sustainable by using strategic urban planning to set the vision, to identify the challenges and the problematic areas and to set new goals and objectives in order to plan and build to future proof the complexity of an urban system. For answering the questions of this article we use two case studies Rotterdam (Netherlands) and Thessaloniki (Greece), using a literature review and researches conducted alongside with a benchmarking of their resilient strategies as both of the cities are members of the Resilient Cities Network. From a different perspective of resilient thinking, both of the cities have managed to use resilience as a marketing image for further sustainable development.

In this paper, a technique of image watermarking using multiple images as watermarks is presented. The technique is based on transform domain functions including discrete wavelet transform (DWT), discrete cosine transform (DCT) and singular value decomposition (SVD) with an image as the host signal i.e. the watermarks will be used as proofs of the authenticity of the host image. The technique is executed by performing multilevel DWT followed by applying DCT and SVD to both the host and watermark. Multiple watermarks are used for the insurance of better security level. The scheme is immune to common image processing operations & some attacks and exhibits PSNR of 108.3781dB, normalized cross correlation (NCC) over 0.99 and normalized correlation (NC) over 0.99.

The aim of the research was getting to know the risk of dependency on physical exercising in individual sportspeople and the relationship with body dissatisfaction and motivation. 225 triathletes, swimmers, cyclists and athletes- with ages going from 18 to 63 years old took part in the research, of which 145 were men (M = 35.57 ±10.46 years) and 80 women (M = 32.83 ±10.31 years). The EDS-R was used to study the dependency on exercising, BSQ to study body dissatisfaction, BREQ-3 to know the motivation of participants and BIAQ to analyse conducts of avoidance to body image. The obtained results show that 8.5% of the subjects had risk of dependency on exercising and that 18.2% tend to have corporal dissatisfaction, without meaningful differences in the kind of sport they practiced. However, there were important differences concerning the dependency on physical exercise (15% vs 4.8%) and body dissatisfaction (31.1% vs 11%) in relation to sex, being the higher percentage referring to women. The introjected regulation and the conduct of food restriction were the predictor variables of the dependency on exercising and corporal dissatisfaction.

Much has been learned about the interaction between myosin and actin through biochemistry, in vitro motility assays and cryo-electron microscopy of F-actin decorated with myosin heads. Comparatively less is known about actin-myosin interactions within the filament lattice of muscle, where myosin heads function as independent force generators and thus most measurements report an average signal from multiple biochemical and mechanical states. All of the 3-D imaging by electron microscopy that has revealed the interplay of the regular array of actin subunits and myosin heads within the filament lattice has been accomplished using the flight muscle of the large waterbug Lethocerus sp. Lethocerus flight muscle possesses a particularly favorable filament arrangement that enables all the myosin cross-bridges contacting the actin filament to be visualized in a thin section. This review covers the history of this effort and the progress toward visualizing the complex set of conformational changes that myosin heads make when binding to actin in several static states as well as fast frozen actively contracting muscle. The efforts have revealed a consistent pattern of changes to the myosin head structures determined by X-ray crystallography needed to explain the structure of the different acto-myosin interactions observed in situ.

At present, business strategies in SMEs (Small and medium enterprises) are crucial for consolidation in highly competitive markets, in achieving a better image and in business profitability. One of the strategies that have the most success and business success are sustainable practices and social responsibility such as: ISO 14001 and ISO 26001. The literature related to sustainable business is based mainly on the theory of resources and capabilities, and in theory based on Stakeholders. These currents state that companies should focus on profitable strategies to ensure significant and long-term results, in order to achieve organizational and financial results for stakeholders. In this work, the sample consists of 215 companies from the commerce, services and industry sectors, located in the southern region of the State of Sonora in Mexico. The objective of the work is to analyze the influence of ISO 14001 and 26001 standards on the image and profitability of SMEs. The statistical analysis of the data has been carried out through the linear regression technique by OLS (Ordinary Least Squares). The findings prove that the ISO 14001 standard is the one that most influences the improvement of the business image and the level of profitability of the SME. In addition, we discovered that ISO 26001 has a partial influence on the image and profitability of the SME.

As the demand for a more sustainable society increases, adopting a sustainable banking approach serves as a competitive advantage for banks that are focused on attaining bank loyalty. This study revolves around understanding the role of sustainable banking practices on bank loyalty, while exploring the mediating effect of corporate image in the relationship between sustainable banking practices and bank loyalty. 511 data derived from customers of the banking sector was adopted for this study. Result from the structural equation modeling shows that sustainable banking practices positively and directly affects bank loyalty and corporate image, corporate image directly and positively affect bank loyalty, and also mediates in the relationship between sustainable banking practices and bank loyalty.

An effective on-board cloud detection method in small satellites would greatly improve the downlink data transmission efficiency and reduce the memory cost. In this paper, an ensemble method combining a lightweight U-Net with wavelet image compression is proposed and evaluated. The red, green, blue and infrared waveband images from Landsat-8 dataset are trained and tested to estimate the performance of proposed method. The LeGall-5/3 wavelet transform is applied on the dataset to accelerate the neural network and improve the feasibility of on-board implement. The experiment results illustrate that the overall accuracy of the proposed model achieves 97.45% by utilizing only four bands. Tests on low coefficients of compressed dataset have shown that the overall accuracy of the proposed method is still higher than 95%, while its inference speed is accelerated to 0.055 second per million pixels and maximum memory cost reduces to 2Mb. By taking advantage of mature image compression system in small satellites, the proposed method provides a good possibility of on-board cloud detection based on deep learning.

The deterioration of living conditions is highlighting the benefits of urban greenery for creating sustainable cities. Using Washington D.C. as a case, we demonstrate the performance of GVI and examine disparities in greening surrounding affordable housing. With the pre-trained segmentation model, we generated GVI based on 54,691 Street View images and applied the Spearman’s correlation analysis to examine LST cooling effect. We found that LST shows significant negative correlations (p<0.001) with GVI and newly developed GVI-3D with coefficients of -0.60 and -0.73. The distribution of GVI in residential zoning was polarised, affordable housing projects have less greenery. In conclusion, we argue that it is of practical significance to monitor GVI instead of large scale NDVI for urban environments due to its controllability and human-centric attributes.

Popular geo-computer vision works make use of aerial imager with sizes ranging from 64 × 64 to 1024 × 1024 pixels without any overlap, although the learning process of deep learning models can be affected by the reduced semantic context or the lack of information near image boundaries. In this work, the impact of three image sizes (256 × 256, 512 × 512, and 1024 × 1024 pixels) and two image overlap levels (no overlap and 12.5% overlap) on the performance of road classification models was statistically evaluated. For this, two convolutional neural networks used in various tasks of geospatial object extraction were trained (using the same hyperparameters) on a large dataset (containing aerial image data covering 8650 km² of the Spanish territory that was labelled with binary road information) under twelve different scenarios, each scenario featuring a different combination of tile size and overlap. To assess their generalisation capacity, the performance of all resulting models was evaluated on a data from novel areas covering approximately 825 km². The performance metrics obtained were analysed using appropriate descriptive and inferential statistical techniques to evaluate the impact of the performance at distinct levels of the fixed factors (tile size, tile overlap, neural network architecture). Statistical tests were applied to study the main and interaction effects of the fixed factors on the performance. A significance level of 0.05 was applied to all the null hypothesis tests. The results were highly significant for the main effects (p-values lower than 0.001), while the two-way and three-way interaction effects among them had different levels of significance. The results indicate that the training of road classification models on images with a higher tile size (more semantic context) and tile overlap (additional border context and continuity) significantly impacts their performance. The best model was trained on a dataset featuring tiles with a size of 1024 × 1024 pixels and a 12.5% overlap and achieved a loss value of 0.0984, an F1 score of 0.8728, and an ROC-AUC score of 0.9766, together with an error rate of 3.5% on the test set.

The paper describes the new method for image compression based on coordinated group signal transformation. The algorithm is a type of difference coding. Coordinated processing significantly simplifies the difference signal conversion scheme using a single group codec for all signals. The described method considers color channels as correlated signals of a multi-channel communication system. The specifics of the processed data required modification of the coordinated group processing algorithm. First, we changed how to calculate the difference signals to prevent data loss. Secondly, the group codec was supplemented with a neural network to improve the quality of reconstructed images. We considered the following types of neural networks: fully connected, recurrent, convolution, and convolution in the Fourier space. Based on the simulation results, it is best to use fully connected neural networks if the goal is to minimize processing delay time. These networks have a response time of 13 ms. On the other hand, if the priority is to improve quality in cases where delays are not critical, then convolution neural networks in the Fourier space should be used. These networks have a response time of 140 ms and are of significant interest.

Detector-based spectral CT offers the possibility of obtaining spectral information from which discrete acquisitions at different energy levels can be derived, yielding so-called virtual monoenergetic images (VMI). In this study, we aimed to develop a jointly optimized deep learning framework based on dual-energy CT pulmonary angiography (DE-CTPA) data to generate synthetic monoenergetic images (SMI) for improving automatic pulmonary embolism (PE) detection in single-energy CTPA scans. For this purpose, we used two data sets: our institutional DE-CTPA data set D1 comprising polyenergetic arterial series and the corresponding VMI at low-energy levels (40 keV) with 7,892 image pairs, and a 10% subset of the 2020 RSNA Pulmonary Embolism Detection Challenge data set D2, which consisted of 161,253 polyenergetic images with dichotomous slice-wise annotations (PE/no PE). We trained a fully convolutional encoder-decoder on D1 to generate SMI from single-energy CTPA scans of D2, which were then fed into a ResNet50 network for training of the downstream PE classification task. The quantitative results on the reconstruction ability of our framework revealed high-quality visual SMI predictions with reconstruction results of 0.984 ± 0.002 (structural similarity) and 41.706 ± 0.547 dB (peak-signal-to-noise ratio). PE classification resulted in an AUC of 0.84 for our model, which achieved improved performance compared to other naive approaches with AUCs up to 0.81. Our study stresses the role of using joint optimization strategies for deep learning algorithms to improve automatic PE detection. The proposed pipeline may prove to be beneficial for computer-aided detection systems and could help rescue CTPA studies with suboptimal opacification of the pulmonary arteries from single-energy CT scanners.

Advancements in deep learning and availability of medical imaging data have led to use of CNN based architectures in disease diagnostic assisted systems. In spite of the abundant use of reverse transcription-polymerase chain reaction (RT-PCR) based tests in COVID-19 diagnosis, CT images offer an applicable supplement with its high sensitivity rates. Here, we study classification of COVID-19 pneumonia (CP) and non-COVID-19 pneumonia (NCP) in chest CT scans using efficient deep learning methods to be readily implemented by any hospital. We report our deep network framework design that encompasses Convolutional Neural Networks (CNNs) and bidirectional Long Short Term Memory (biLSTM) architectures. Our study achieved high specificity (CP: 98.3%, NCP: 96.2% Healthy: 89.3%) and high sensitivity (CP: 84.0%, NCP: 93.9% Healthy: 94.9%) in classifying COVID-19 pneumonia, non-COVID-19 pneumonia and healthy patients. Next, we provide visual explanations for the CNN predictions with gradient-weighted class activation mapping (Grad-CAM). The results provided a model explainability by showing that Ground Glass Opacities (GGO), indicators of COVID-19 pneumonia disease, were captured by our CNN network. Finally, we have implemented our approach in three hospitals proving its compatibility and efficiency.

Advanced image processing will be crucial for emerging technologies such as autonomous driving, where the requirement to quickly recognize and classify objects under rapidly changing, poor visibility environments in real time will be needed. Photonic technologies will be key for next-generation signal and information processing, due to their wide bandwidths of 10’s of Terahertz and versatility. Here, we demonstrate broadband real time analog image and video processing with an ultrahigh bandwidth photonic processor that is highly versatile and reconfigurable. It is capable of massively parallel processing over 10,000 video signals simultaneously in real time, performing key functions needed for object recognition, such as edge enhancement and detection. Our system, based on a soliton crystal Kerr optical micro-comb with a 49GHz spacing with >90 wavelengths in the C-band, is highly versatile, performing different functions without changing the physical hardware. These results highlight the potential for photonic processing based on Kerr microcombs for chip-scale fully programmable high-speed real time video processing for next generation technologies.

This paper presents novel feature descriptors and classification algorithms for automated scoring of HER2 in Whole Slide Images (WSI) of breast cancer histology slides. Since a large amount of processing is involved in analyzing WSI images, the primary design goal has been to keep the computational complexity to the minimum possible level and to use simple, yet robust feature descriptors that can provide accurate classification of the slides. We propose two types of feature descriptors that encode important information about staining patterns and the percentage of staining present in ImmunoHistoChemistry (IHC) stained slides. The first descriptor is called a characteristic curve which is a smooth non-increasing curve that represents the variation of percentage of staining with saturation levels. The second new descriptor introduced in this paper is an LBP feature curve which is also a non-increasing smooth curve that represents the local texture of the staining patterns. Both descriptors show excellent interclass variance and intraclass correlation, and are suitable for the design of automatic HER2 classification algorithms. This paper gives the detailed theoretical aspects of the feature descriptors and also provides experimental results and comparative analysis.

Speckle noise reduction is an important area of research in the field of ultrasound image processing. Several algorithms for speckle noise characterization and analysis have been recently proposed in the area. Synthetic ultrasound images can play a key role in noise evaluation methods as they can be used to generate a variety of speckle noise models under different interpolation and sampling schemes, and can also provide valuable ground truth data for estimating the accuracy of the chosen methods. However, not much work has been done in the area of modelling synthetic ultrasound images, and in simulating speckle noise generation to get images that are as close as possible to real ultrasound images. An important aspect of simulated synthetic ultrasound images is the requirement for extensive quality assessment for ensuring that they have the texture characteristics and gray-tone features of real images. This paper presents texture feature analysis of synthetic ultrasound images using local binary patterns (LBP) and demonstrates the usefulness of a set of LBP features for image quality assessment. Experimental results presented in the paper clearly show how these features could provide an accurate quality metric that correlates very well with subjective evaluations performed by clinical experts.

Global climatic pressures and increased human demands create a modern necessity for efficient and affordable plant phenotyping unencumbered by arduous technical requirements. The analysis and archival of imagery have become easier as modern camera technology and computers are leveraged. This facilitates the detection of vegetation status and changes over time. Using a custom lightbox, an inexpensive camera, and common software, turfgrass pots were photographed in a greenhouse environment over an 8-week experiment period. Subsequent imagery was analyzed for area of cover, color metrics, and sensitivity to image corrections. Findings were compared to active spectral reflectance data and previously reported measurements of visual quality, productivity, and water use. Results indicate that Red Green Blue-based (RGB) imagery with simple controls is sufficient to measure the effects of plant treatments. Notable correlations were observed for corrected imagery, including between a percent yellow color area classification segment (%Y) with human visual quality ratings (VQ) (R = -0.89), the dark green color index (DGCI) with clipping productivity in mg d-1 (mg) (R = 0.61), and an index combination term (COMB2) with water use in mm d-1 (mm) (R = -0.60). The calculation of green cover area (%G) correlated with Normalized Difference Vegetation Index (NDVI) (R = 0.91) and its RED reflectance spectra (R = -0.87). A CIELAB b*/a* chromatic ratio (BA) correlated with Normalized Difference Red-Edge index (NDRE) (R = 0.90), and its Red-Edge (RE) (R = -0.74) reflectance spectra, while a new calculation termed HSVi correlated strongest to the Near-Infrared (NIR) (R = 0.90) reflectance spectra. Additionally, COMB2 significantly differentiated between the treatment effects of date, mowing height, deficit irrigation, and their interactions (p &lt; 0.001). Sensitivity and statistical analysis of typical image file formats and corrections that included JPEG (JPG), TIFF (TIF), geometric lens correction (LC), and color correction (CC) were conducted. Results underscore the need for further research to support image corrections standardization and better connect image data to biological processes. This study demonstrates the potential of consumer-grade photography to capture plant phenotypic traits.

Real-time image processing is crucial in applications such as video surveillance, diagnostic medicine, and autonomous vehicle systems. Parallel computing is essential to address the demands of real-time processing, but challenges persist in its practical implementation. This review analyzes parallel computing techniques applied to real-time image processing, examining parallel architectures and algorithms, identifying effective applications, and evaluating challenges and limitations. The results of this study will contribute to the development of more efficient solutions for real-time image processing. Keywords: Parallel computing, real-time image processing, video surveillance, diagnostic medicine, autonomous vehicle systems.

This work investigates into the enhancement of iris recognition systems through a two-module approach focusing on low level image preprocessing techniques and advanced feature extraction. The first module is dedicated to the preprocessing of iris images, leveraging the Canny algorithm for edge detection followed by the circle-based Hough transform for precise iris extraction. This process, integral to our methodology, ensures the quality and consistency necessary for the subsequent Binary Statistical Image Features (BSIF) analysis. The second module employs the BSIF technique, incorporating domain-specific filters trained on iris-specific data, for robust biometric identification. By combining these advanced image preprocessing techniques, the proposed method addresses key challenges in iris recognition, such as occlusions, varying pigmentation, and textural diversity. Experimental results on the Human-inspired Domain-specific Binarized Image Features (HDBIF) Dataset composed of 1892 iris images demonstrate the effectiveness of the developped approach, with potential future improvements including adaptive algorithms and machine learning integrations for enhanced performance in diverse and unpredictable real-world applications. The novel contributions of this paper lie in the provision of a reproducible research framework, making the iris-domain-specific BSIF filters, training patches, testing database, and source codes publicly available for further application and study.

Radiotherapy commonly utilizes CBCT for patient positioning and treatment monitoring. CBCT is deemed to be secure for patients, making it suitable for the delivery of fractional doses. However, limitations such as a narrow field of view, beam hardening, scattered radiation artifacts, and variability in pixel intensity hinder the direct use of raw CBCT for dose recalculation during treatment. To address this issue, reliable correction techniques are necessary to remove artifacts and remap pixel intensity into HU values. This study proposes a deep-learning framework for calibrating CBCT images acquired with narrow FOV systems and demonstrates its potential use in proton treatment planning updates. Cycle-consistent GAN processes raw CBCT to reduce scatter and remap HU. Monte Carlo simulation is used to generate CBCT scans, enabling the possibility to focus solely on the algorithm’s ability to reduce artifacts and cupping effects without considering intra-patient longitudinal variability and producing a fair comparison between planning CT and calibrated CBCT dosimetry. To showcase the viability of the approach using real-world data, experiments were also conducted using real CBCT. Tests were performed on a publicly available dataset of 40 patients who received ablative radiation therapy for pancreatic cancer. The simulated CBCT calibration led to a difference in proton dosimetry of less than 2%, compared to the planning CT. The potential toxicity effect on the organs at risk decreased from about 50% (uncalibrated) up the 2% (calibrated). The gamma pass rate at 3%/2mm produced an improvement of about 37% in replicating the prescribed dose before and after calibration (53.78% vs 90.26%). Real data also confirmed this with slightly inferior performances for the same criteria (65.36% vs 87.20%). These results may confirm that generative artificial intelligence brings the use of narrow FOV CBCT scans incrementally closer to clinical translation in proton therapy planning updates.

Skin image analysis using artificial intelligence (AI) has recently attracted significant research interest, particularly for analyzing skin images captured by mobile devices. Acne is one of the most common skin conditions with profound effects in severe cases. In this study, we developed an AI system called AcneDet for automatic acne object detection and acne severity grading using facial images captured by smartphones. AcneDet includes two models for conducting two tasks: (1) a Faster R-CNN-based deep learning model for the detection of acne lesion objects of four types including blackheads/whiteheads, papules/pustules, nodules/cysts, and acne scars; and (2) a LightGBM machine learning model for grading acne severity using the Investigator’s Global Assessment (IGA) scale. The output of the Faster R-CNN model, i.e., the counts of each acne type, were used as input for the LightGBM model for acne severity grading. A dataset consisting of 1,572 labeled facial images captured by both iOS and Android smartphones was used for training. The results show that the Faster R-CNN model achieves a mAP of 0.54 for acne object detection. The mean accuracy of acne severity grading by the LightGBM model is 0.85. With this study, we hope to contribute to the development of artificial intelligent systems that are able to help acne patients understand more about their conditions and support doctors in acne diagnosis.

Deep Learning is a recent and important addition to the computational toolbox available for image reconstruction in fluorescence microscopy. We review state-of-the-art applications such as image restoration, super-resolution, and light-field imaging, and discuss how the latest Deep Learning research can be applied to other image reconstruction tasks such as structured illumination, spectral deconvolution, and sample stabilisation. Despite its successes, Deep Learning also poses significant challenges, has often misunderstood capabilities, and overlooked limits. We will address key questions, such as: What are the challenges in obtaining training data? Can we discover structures not present in the training data? And, what is the danger of inferring unsubstantiated image details?

The key to simulating soil erosion is to calculate the vegetation cover (C) factor. Methods that apply remote sensing to calculate C factor at regional scale cannot directly use the C factor formula. That is because the C factor formula is obtained by experiment, and needs the coverage ratio data of croplands, woodlands and grasslands at standard plot scale. In this paper, we present a C factor conversion method from a standard plot to a km-sized grid based on large sample theory and multi-scale remote sensing. Results show that: 1) Compared with the existing C factor formula, our method is based on the coverage ratio of croplands, woodlands and grasslands on a km-sized grid, takes the C factor formula obtained from the standard plot experiment and applies it to regional scale. This method improves the applicability of the C factor formula, and can satisfy the need to simulate soil erosion in large areas. 2) The vegetation coverage obtained by remote sensing interpretation is significantly consistent (paired samples t-test, t = −0.03, df = 0.12, 2-tail significance p < 0.05) and significantly correlated with the measured vegetation coverage. 3) The C factor of the study area is smaller in the middle, southern and northern regions, and larger in the eastern and western regions. The main reason for that is the distribution of woodlands, the Hunshandake and Horqin sandy lands and the valleys affected by human activities. 4) The method presented in this paper is more meticulous than the C factor method based on the vegetation index, improves the applicability of the C factor formula, and can be used to simulate soil erosion on large scale and provide strong support for regional soil and water conservation planning.

Breast cancer is among the most prevalent cancers in Indonesia. One of the methods for early detection of breast cancer is utilizing ultrasound images to classify breast conditions into normal, benign tumors, or cancer. The advent of deep learning technology facilitates the image analysis process, such as the use of Transfer Learning Convolutional Neural Networks (CNNs). Generally, CNN models require large and balanced datasets to perform well in classification tasks. However, medical datasets like breast ultrasound images tend to be limited and imbalanced. The Wasserstein GAN (WGAN) is a generative data augmentation method capable of producing synthetic images by learning the pattern of the distribution of real image data. The implementation of the Wasserstein distance results in the training process of WGAN demonstrating stability from epochs 3000, 2500, and 3000 out of a total of 5000 epochs. The quality of the synthetic images improves with an increasing number of training iterations. By using WGAN, all of the evaluation metrics of each classifier are increasing, with the best accuracy score is achieved by VGG16 model with 83.33% accuracy

Explainability is essential for AI models, especially in clinical settings where understanding the model's decisions is crucial. Despite their impressive performance, black-box AI models are unsuitable for clinical use if their operations cannot be explained to clinicians. While deep neural networks (DNNs) represent the forefront of model performance, their explanations are often not easily interpretable by humans. On the other hand, using hand-crafted features extracted to represent different aspects of the input data and traditional machine learning models are generally more understandable. However, they often lack the effectiveness of advanced models due to human limitations in feature design. To address this, we propose ExShall-CNN, a novel explainable shallow convolutional neural network for medical image processing. This model enhances hand-crafted features to maintain human interpretability while achieving performance levels comparable to advanced deep convolutional networks, such as U-Net, for medical image segmentation. ExShall-CNN and its source code are publicly available at: https://github.com/MLBC-lab/ExShall-CNN

Deep learning methods have been widely applied to time series classification tasks. Although deep learning methods perform excellently on these tasks, they require much training data. Labeling time series data for training is very time-consuming and labor-intensive. Active learning can be used to select the most informative data for labeling in time series classification tasks to save human labeling efforts. In this paper, we propose a new active learning (AL) framework for time series data. First, a temporal classifier for pixel-level temporal classification tasks is designed. Next, We propose an effective active learning method to select informative time series samples for labeling, which combines representativeness and uncertainty. For representativeness, We use the K-shape method to cluster time series data. For uncertainty, we construct an auxiliary deep network to evaluate the uncertainty of unlabeled data. The features of the middle hidden layer with rich temporal information of the classifier will be fed into the auxiliary deep network, which is equipped with a self-attention mechanism to utilize the temporal dependencies of time series data fully. Then, we define a new loss function with the aim of improving the deep model’s performance. Finally, the proposed method in this paper was evaluated on two temporal remote-sensing image datasets. The results demonstrate a significant advantage of our method over other approaches to time series data. Code is available at https://github.com/Fighting-Golion/time_series_active_learning

Resilience is an important determinant of the quality of life in adults, among others in the sphere of cognitive activities. Developments in brain imaging technology make it possible to acquire a more accurate understanding of the neuronal mechanisms of these correlations. The aim of this study was to verify whether the level of mental resilience would differentiate reading comprehension performance when using different information carriers. More than 150 adults were surveyed with the use of two questionnaires (original and standardised) and near-infrared spectroscopy (fNIRS). The results obtained did not show differences in correct responses between the groups of individuals with different levels of resilience, however, the results in the group of subjects with moderate levels were differentiated by the type of carrier. Further analysis showed that the results referred to above resulted from different neuronal mechanisms. The differences affected many areas of the brain and also depended on the type of information carrier (medium). They affected, among others, attentional areas as well as areas directly involved in the reading process. This may result in increased energy consumption during the examined activities, which can result in greater fatigue, accelerated job burnout and progressive concentration difficulties.

Our study focused on investigating the impact of Instagram use on body satisfaction and self-esteem among participants aged 20 to 40 years (N = 95) through a cross-sectional survey design in which the Rosenberg Self-Esteem Scale, the Body Shape Questionnaire (BSQ), and ad hoc designed questions were administered. The results highlight the influence of Instagram on body dissatisfaction (p = .005) but not on self-esteem (p = .211). Gender was not found to be an influential factor (p = .173). However, it was found that greater body satisfaction correlates with higher self-esteem and vice versa (p < .001). Finally, we concluded that the number of hours users in the sample spend on Instagram is related to greater body dissatisfaction but not to lower self-esteem.

A free-fall baited camera lander was launched for the first time on the Cosmonaut Sea shelf of East Antarctica at the depth of 694 m during the 38th Chinese National Antarctic Research Expedition (CHINARE) in 2022. We identified 31 unique taxa (23 of invertebrates and 8 of fishes) belonging to 8 phyla from 2403 pictures and 40 videos. The Antarctic jonasfish (Notolepis coatsi) was the most frequently observed fish taxa. Ten species of vulnerable marine ecosystem (VME) taxa were observed, accounting for 32% of all species. The maximum number (MaxN) of Natatolana meridionalis individuals per image frame was ten, and they were attracted to the bait. The macrobenthic community type was sessile suspension feeders with associated fauna (SSFA), which was shaped by the muddy substrata with scattered rocks. Rocks served as the best habitats for sessile fauna. The study reveals the megafauna community and their habitat by image survey in the Cosmonaut Sea for the first time. It helped us obtain Antarctic biodiversity baselines and monitoring data for future’s ecosystem health assessment and better protection.

Olive fruits at different ripening stages give rise to various table olive products and oil qualities. Therefore, developing an efficient method for recognizing and sorting olive fruits based on their ripening stages can greatly facilitate postharvest processing. This study introduces an automatic computer vision system that utilizes deep learning technology to classify the `Roghani` Iranian olive cultivar into five ripening stages using color images. The developed model employs convolutional neural networks (CNN) and transfer learning based on the Xception architecture and ImageNet weights as the base network. The model was fine-tuned by testing multiple configurations of well-known CNN layers. To minimize overfitting and enhance model generality, data augmentation techniques were employed. By considering different optimizers and two image sizes, four final candidate models were generated. These models were then compared in terms of loss and accuracy on the test dataset, classification performance (classification report and confusion matrix), and generality. All four candidates exhibited high accuracies ranging from 86.93% to 93.46% and comparable classification performance. In all models, at least one class was recognized with 100% accuracy. However, by taking into account the risk of overfitting, two models were discarded. Finally, a model with an image size of 224 × 224 and an SGD optimizer, which had a loss of 1.23 and an accuracy of 86.93%, was selected as the preferred option. The results of this study offer robust tools for automatic olive sorting systems, simplifying the differentiation of olives at various ripening levels for different post-harvest products.

In recent years, significant advancements in the field of machine learning have influenced the domain of image restoration. While these technological advancements present prospects for improving the quality of images, they also present difficulties, particularly the proliferation of manipulated or counterfeit multimedia information on the internet. The objective of this paper is to provide a comprehensive review of existing inpainting algorithms and forgery detections, with a specific emphasis on techniques that are designed for the purpose of removing objects from digital images. In this study, we will examine various techniques encompassing conventional texture synthesis methods, as well as those based on neural networks. Furthermore, we will explore the artifacts associated with the identification of modified photos and present the artifacts frequently introduced by the inpainting procedure and assess the state-of-the-art technology for detecting such modifications. Lastly, we shall look at the available datasets and how the methods compare with each other. Having covered all of the above, the final outcome of this study is to provide a comprehensive perspective on the abilities and constraints to detect images for which an inpainting object removal method was applied.

The study focuses on the crucial aspect of lossless compression for FAST pulsar search data. The deep generative model PixelCNN, stacking multiple masked convolutional layers, achieves neural network autoregressive modeling, making it one of the most excellent image density estimators. However, the local nature of convolutional networks causes PixelCNN to concentrate only on nearby information, neglecting important information at greater distances. Although deepening the network can broaden the receptive field, excessive depth can compromise model stability, leading to issues like gradient degradation. To address these challenges, the study combines causal attention modules with residual connections, proposing the Causal Residual Attention Module to enhance the PixelCNN model. This innovation not only resolves convergence problems arising from network deepening but also widens the receptive field. It effectively utilizes global features, particularly capturing vertically correlated features prominently present in subgraphs of candidates. This significantly enhances its capability to model pulsar data.In the experiments, the model is trained and validated using the HTRU1 dataset. The study compares the average negative log-likelihood score with baseline models like GMM, STM, and PixelCNN. The results demonstrate the superior performance of the our model over other models. Finally, the study introduces the practical compression encoding process by combining the proposed model with arithmetic coding.

Many university female students are concerned about their bodies. Body image perception has become a public health issue globally. This study aimed to explore factors contributing to body image dissatisfaction among female students at the University of Venda. The study was qualitative in nature and employed exploratory research design. A sample of 10 female students enrolled at the University of Venda were identified using convenience sampling method. A pre-tested, semi-structured interview guide was used to collect data and thematic content analysis technique was used to analyse the collected data. The findings of the study showed that body comparison, societal beauty standards, social media, and body shaming by family and friends were the main factors contributing to student’s body image dissatisfaction. The findings further revealed that lack of self-confidence, stress, avoidance, anxiety and depressive symptoms were the challenges faced by students with body image dissatisfaction. Acceptance. Self-care, and healthy diet were identified as coping strategies to help deal with the challenges of student’s body image dissatisfaction. Conclusively, students should be encouraged to seek professional help timeously, to help navigate their body image concerns to avoid decline in their daily functioning.

Colombia is noteworthy as a biodiversity hotspot, featuring an extraordinary number of en-demic orchids, birds, and butterflies. This exploratory study examines the perceptions of desti-nation image considering the cognitive and affective image in predicting behavioral intentions of ecotourists through symmetric data analysis. Using Partial Least Squares (PLS), the author(s) analyzed 64 survey responses collected of rural areas, including a new 15 statement scale spe-cialized on birdwatching. The findings support the reliability of the model, symmetric analysis presents the higher influence of emotions and affections in increasing intentions of recommen-dation, considering birdwatching as based on personal relationships. Additionally, the cognitive image for the birders despite representing destination attributes or sets of destination resources of a mental picture does not have the same impact on behavioral intentions. Therefore, manag-ers should develop positioning strategies based on the generation of emotions, because bird-watching tourists seek to have more emotional experiences.

In this study, we explore the potential of using a straightforward neural network inspired by the retina model to efficiently restore low-light images. The retina model imitates the neurophysiological principles and dynamics of various optical neurons. Our proposed neural network model reduces the computational overhead compared to traditional signal-processing models while achieving results similar to complex deep learning models from a subjective perceptual perspective. By directly simulating retinal neuron functionalities with neural networks, we not only avoid manual parameter optimization but also lay the groundwork for constructing artificial versions of specific neurobiological organizations.

Recent advancements in Natural Language Processing (NLP), particularly in Large Language Models (LLMs), associated with deep learning-based computer vision techniques, have shown substantial potential for automating a variety of tasks. One notable model is Visual ChatGPT, which combines ChatGPT’s LLM capabilities with visual computation to enable effective image analysis. The model’s ability to process images based on textual inputs can revolutionize diverse fields. However, its application in the remote sensing domain remains unexplored. This is the first paper to examine the potential of Visual ChatGPT, a cutting-edge LLM founded on the GPT architecture, to tackle the aspects of image processing related to the remote sensing domain. Among its current capabilities, Visual ChatGPT can generate textual descriptions of images, perform canny edge and straight line detection, and conduct image segmentation. These offer valuable insights into image content and facilitate the interpretation and extraction of information. By exploring the applicability of these techniques within publicly available datasets of satellite images, we demonstrate the current model’s limitations in dealing with remote sensing images, highlighting its challenges and future prospects. Although still in early development, we believe that the combination of LLMs and visual models holds a significant potential to transform remote sensing image processing, creating accessible and practical application opportunities in the field.

Vision loss can be avoided if diabetic retinopathy (DR) is diagnosed and treated promptly. Following are the main 5 DR stages: none, moderate, mild, proliferate, and severe. In this study, a deep learning (DL) model is presented that diagnoses all 5 stages of DR with more accuracy than previous methods. The suggested method presents two scenarios: case 1 with image enhancement using contrast limited adaptive histogram equalization (CLAHE) filtering algorithm in conjunction with an Enhanced Super-resolution generative adversarial network (ESRGAN), and case 2 without image enhancement; augmentation techniques are then performed to generate a balanced dataset utilizing the same parameters for both cases. Using Inception-V3 applied to the Asia Pacific Tele-Ophthalmology Society (APTOS) datasets, the developed model achieved an accuracy of 98.7% for case 1 and 80.87% for case 2, which is greater than existing methods for detecting the five stages of DR. It was demonstrated that using CLAHE and ESRGAN improves a model's performance and learning ability.

In the field of forensic imaging, it is important to be able to extract a “camera fingerprint” from one or a small set of images known to have been taken by the same camera (image sensor). Ideally, that fingerprint would be used to identify an individual source camera. Camera fingerprint is based on certain kind of random noise present in all image sensors that is due to manufacturing imperfections and thus unique and impossible to avoid. PRNU (Photo-Response Non-Uniformity) has become the most widely used method for SCI (Source Camera Identification). In this paper, we design a set of “attacks” to a PRNU based SCI system and we measure the success of each method. We understand an attack method as any processing that alters minimally image quality and that is designed to fool PRNU detectors (or, generalizing, any camera fingerprint detector). The PRNU based SCI system was taken from an outstanding reference that is publicly available.

Depth maps produced by LiDAR based approaches are sparse. Even high-end LiDAR sensors produce highly sparse depth maps, which are also noisy around the object boundaries. Depth completion is the task of generating a dense depth map from a sparse depth map. While the traditional approaches focus on directly completing this sparsity from the sparse depth maps, modern techniques use RGB images as a guidance tool to resolve this problem. Whilst many others rely on affinity matrices for depth completion. Based on these approaches, we have sub-divided the literature into two major categories; traditional approaches and backbone-based approaches. The latter is further sub-divided into two-branch, and spatial propagation approaches. The two-branch approaches still have a sub-category named guided-kernel approaches. In this paper, for the first time ever we present a comprehensive survey of depth completion methods. We present a novel taxonomy of depth completion approaches, review and detail different state-of-the art techniques within each category for depth completion of LiDAR data, and provide quantitative results for the approaches on KITTI and NYUv2 depth completion benchmark datasets.

Video editing is a high-required job, for it requires skilled artists or workers equipped with plentiful physical strength and multidisciplinary knowledge, such as cinematography, aesthetics. Thus gradually, more and more researches focus on proposing semi-automatical and even fully automatical solutions to reduce workloads. Since those conventional methods are usually designed to follow some simple guidelines, they lack flexibility and capability to learn complex ones. Fortunately, the advances of computer vision and machine learning make up the shortages of traditional approaches and make AI editing feasible. There is no survey to conclude those emerging researches yet. This paper summaries the development history of automatic video editing, and especially the applications of AI in partial and full workflows. We emphasizes video editing and discuss related works from multiple aspects: modality, type of input videos, methology, optimization, dataset, and evaluation metric. Besides, we also summarize the progresses in image editing domain, i.e., style transferring, retargeting, and colorization, and seek for the possibility to transfer those techniques to video domain. Finally, we give a brief conclusion about this survey and explore some open problems.

Microscopes based on dielectric mesoscale particles, using the effect of a photonic jet or terajet in the terahertz range, are a promising tool for overcoming the diffraction limit. However, the image they generate has limited contrast, which limits the application of this method. In this letter, we demonstrate that it is possible to increase the contrast of an image based on dielectric mesoscale particles that provide the formation of photonic hooks. In this case, the illumination of the object is carried out by an oblique incidence of subwavelength terajet, which significantly (more than 2 times) increases the contrast of the image.

Forest fire identification is important for forest resource protection. Effective monitoring of forest fires requires the deployment of multiple monitors with different viewpoints, while most traditional recognition models can only recognize images from a single source. By ignoring the information from images with different viewpoints, these models produce high rates of missed and false alarms. In this paper, we propose a graph neural network model based on the similarity of dynamic features of multi-view images to improve the accuracy of forest fire recognition. The input features of the nodes on the graph are converted into relational features of different gallery pairs by establishing pairs (nodes) representing different viewpoint images and gallery images. The new feature library relationship is used to update the image gallery with dynamic features in order to achieve the estimation of similarity between images and improve the image recognition rate of the model. In addition, to reduce the complexity of image pre-processing process and extract key features in images effectively, this paper also proposes a dynamic feature extraction method for fire regions based on image segment ability. By setting the threshold value of HSV color space, the fire region is segmented from the image, and the dynamic features of successive frames of the fire region are extracted. The experimental results show that, compared with the baseline method Resnet, this paper's method is more effective in identifying forest fires, and its recognition accuracy is improved by 2%. And the scheme of this paper can adapt to different forest fire scenes, with better generalization ability and anti-interference ability.

The concept of destination image is closely related to the brand image of the destination. A good image is a step in branding the destination. The image of the destination can be a primary, sec-ondary or global one, the latter incorporating the first two. The sustainability of a positive image of the destination is based on both a positive secondary image and a positive global image. The purpose of this research is to analyze separately the two types of images for a given tourist des-tination that has registered in recent years a remarkable increase in the number of visitors. The research is based on a questionnaire-based survey of a sample of 607 people. The collected data were processed with SPSS and the results show significant differences between the two types of images (secondary image and global image), a dangerous situation in the medium and long term for destination management. The nuances in the perception of the image of the destination on the two types of respondents (who experienced respectively who did not experience the destination) can be explained by the aggressive strategy of promoting the tourist destination, but inefficient strategy for younger age groups. The study allows the formulation of conclusions and measures to correct the situation.

The genetic development of commercial broiler led to body misconfiguration and consequent walking disabilities, mainly at the slaughter age. The present study aimed to identify broiler locomotion ability using image analysis automatically. A total of 40 broiler 40 d-old were placed to walk on a specially built runway, and their locomotion was recorded. An image segmentation algorithm was developed, and the coordinates of the bird's center of mass were extracted from the segmented images for each frame analyzed, and the Unrest Index (UI) was applied. We calculated the center of mass's movement of the broiler walking's lateral images, therefore, capturing the bird's displacement speed in the onward direction. Results indicated that broiler speed on the runway tends to decrease with the increase of the gait score. The locomotion did not differ between males or females. The proposed algorithm was efficient if predicting the broiler gait score based on their displacement speed.

This work presents an analytical methodology to predict meat juiciness (discriminant semi-quantitative analysis using groups of intervals of intramuscular fat) and intramuscular fat (regression analysis) in Longissimus thoracis et lumborum (LTL) muscle of Bísaro pigs using as independent variables the animal carcass weight and parameters from color and image analysis. These are non-invasive and non-destructive techniques which allow development of rapid, easy and inexpensive methodologies to evaluate pork meat quality in a slaughterhouse. The proposed predictive supervised multivariate models were non-linear. Discriminant mixture analysis to evaluate meat juiciness by classified samples into three groups—0.6 to 1.1%; 1.25 to 1.5%; and, greater than 1.5%. The obtained model allowed 100% of correct classifications (92% in cross-validation with seven-folds with five repetitions). Polynomial support vector machine regression to determine the intramuscular fat presented R2 and RMSE values of 0.88 and 0.12, respectively in cross-validation with seven-folds with five repetitions. This quantitative model (model’s polynomial kernel optimized to degree of three with a scale factor of 0.1 and a cost value of one) presented R2 and RSE values of 0.999 and 0.04, respectively. The overall predictive results demonstrated the relevance of photographic image and color measurements of the muscle to evaluate the intramuscular fat, rarther than the usual time-consuming and expensive chemical analysis.

In rye, there is a considerable variety of grain color which is determined by the diversity of compounds localized in different parts of the grain (caryopsis) - pericarp, testa, and aleurone. The localization of anthocyanins and proanthocyanidins was analyzed in 26 rye samples with identified anthocyanin genes, along with the analysis of CIE color coordinates. The Grain Scan program [1] was used to analyze images of individual grains. The localization of anthocyanins and proanthocyanidins was studied on longitudinal and cross sections of grains using light microscopy and MALDI-imaging. The violet-grained samples contain anthocyanins in the pericarp, and the green-grained samples contain anthocyanins in the aleurone layer. The green, violet and yellow-grained rye, with the exception of two anthocyaninless mutants vi3 and vi6, shows the presence of proanthocyanidins in the brown-colored testa. Four main color groups of the rye grains (yellow, green, brown, violet) could be differentiated using the color coordinate h° (hue angle). Interspecies and intraspecies variability for the localization of colored flavonoids in cereal grains is discussed.

Within the spectrum of a natural image, the amplitude of modulation decreases with spatial frequency. The speed of such an amplitude decrease, or the amplitude spectrum slope, of an image affects the perceived aesthetic value. Additionally, a human observer would consider a symmetric image more appealing than they do an asymmetric one. We investigated how these two factors jointly affect aesthetic preferences by manipulating both the amplitude spectrum slope and the symmetric level of images to assess their effects on aesthetic preference on a 6-point Likert scale. Our results showed that the preference ratings increased with the symmetry level but had an inverted U-shape relation to amplitude spectrum slope. In addition, a strong interaction existed between symmetry level and amplitude spectrum slope on preference rating, in that symmetry can amplify the amplitude spectrum slope’s effects. Such effects can be described by a quadratic function of the spectrum slope. That is, preference is an inverted U-shape function of spectrum slope whose intercept is determined by the number of symmetry axis. In addition, the interaction between the two factors is manifested as the modulation depth of the quadratic function.

With the widespread use of mobile phones, increasing use of mobile applications in different areas of life have gained a large place. Between the reasons of the preference of the rapid increasing number of apps in the application markets of the different platforms, the aesthetic appereance of the icons that apps have perhaps the most important one. In this study, the visual symmetrical side of the icons of the most downloaded apss which are developed for preparing to Public Servant Exam in Turkey is emphasized. Two different types of data are obtained with working on the icons with image processing technique by using Mathworks Matlab program and survey method which is applied on Korkut Ata University students. By comparing the obtained data with the binary logistic regression method, it was determined that the visual symmetry in the apps’ icons partially contributed to the aesthetic appreciation.

Online social networking techniques and large-scale multimedia retrieval are developing rapidly, which not only has brought great convenience to our daily life, but generated, collected, and stored large-scale multimedia data as well. This trend has put forward higher requirements and greater challenges on massive multimedia retrieval. In this paper, we investigate the problem of image similarity measurement, which is one of the key problems of multimedia retrieval. Firstly, the definition of similarity measurement of images and the related notions are proposed. Then, an efficient similarity measurement framework is proposed. Besides, we present a novel basic method of similarity measurement named SMIN. To improve the performance of similarity measurement, we carefully design a novel indexing structure called SMI Temp Index (SMII for short). Moreover, we establish an index of potential similar visual words off-line to solve to problem that the index cannot be reused. Experimental evaluations on two real image datasets demonstrate that the proposed approach outperforms state-of-the-arts.

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 at harvesting and analysis of numerous plants, which could be automatically done 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.). Section model, 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.

Visible light photography diagnostic images are coloured ex vivo medical images popularly used in Dermatology and Endoscopy for diagnosis and monitoring. The need to protect the integrity of these images as well as associated patient data calls for techniques such as image steganography and watermarking. This research explores and compares the effect of watermarking on the YIQ and YCbCr colour transforms used in processing digital coloured images and video in recent times. Using a new spread spectrum watermarking algorithm, it was found that YIQ has better distortion performance than YCbCr in the order of 3dB while YCbCr had lower BER for accurate watermark retrieval and tamper detection in the order of 1.3 x 10-3.

This study was designed to assess the reliability of geographic information using satellite image information in ungauged basins. For this, this study constructed geographic information using actual gauged data and satellite information data and conducted runoff analysis through S-RAT, a rainfall–runoff model, and performed the comparison and analysis of geographic information and runoff data. For actual gauged data, the gauged geographic information of the Water Resources Management Information System (WAMIS) was collected, and for satellite information, the image information of moderate-resolution imaging spectroradiometer (MODIS) observation sensor loaded on Terra Satellite was collected. As analysis areas, three basins where mountains occupy more than 80% and another three basins where urban areas occupy more than 7% in the Han River basin were selected. According to the analysis result, the gauged information and satellite image information showed great difference in runoff, maximum 50% in peak flood and maximum 17% in total flood, in the rivers with many urban areas, while the runoff difference in the rivers with many mountains showed maximum 13% in peak flood and 4% in total flood. What showed the greatest difference in image information was land use, and it turned out that the MODIS satellite recognized the urban rivers as cities for more than maximum 60% compared to WAMIS-gauged data. Meanwhile, in the forest area, the MODIS satellite image showed error of less than 5% of the WAMIS-gauged data, which indicates that it has higher applicability in Mountain Rivers.

The critical changes in physical appearance during adolescence can considerably influence the self-appraisal of body image. The purpose of this study is to analyze body self-image gender differences in Mediterranean adolescents, and his relationships to the anthropometric characteristics of this population in different phases of the adolescence. Participants were 809 Mediterranean teenagers (396 females) aged 11 to 17. A relative low prevalence of dissatisfaction with body image was observed among healthy urban Mediterranean adolescents (boys 17.3%; girls 22.7%). Girls showed statistically significantly higher mean BSQ scores than boys (M = 61.7, SD = 26.6 versus M = 56.3, SD = 27.1; p < 0.001). Girls in the late adolescence were more often classified as being dissatisfied (31%) in comparison to those in the early adolescent group (19.1%; p < 0.05). There was a good correlation of BSQ scores with all the anthropometric variables in males but not in females.

Aiming at the problems that are poor generalization performance, low retrieval accuracy and large time consumption of existing content-based image retrieval system, the hierarchical image retrieval method based on multi feature fusion is proposed in this paper. The retrieval accuracy rates on Corel5K, UKbeach and Holidays are 68.23(Top 1), 3.73(N-S) and 88.20(mAp), respectively. The experimental results show that the method proposed in this paper can effectively improve the deficiency of single feature retrieval and save time significantly in the premise of a small amount of loss of accuracy.

People often say that beer tastes better from a bottle than from a can. However, one can ask whether this perceived difference is reliable across consumers; And, if so, whether it is purely a psychological phenomenon (associated with the influence of packaging on taste perception), or whether instead it reflects some more mundane physico-chemical interaction between the packaging material (or packing procedure/process) and the contents. We conducted two experiments in order to address these important questions. In the main experiment, 151 participants at the 2016 Edinburgh Science Festival were served a beer in a plastic cup. The beer was either poured from a bottle or can (i.e., a between-participants experimental design was used) and the participants were encouraged to pick up the packaging in order to inspect the label before tasting the beer. The participants rated the perceived taste, quality, and freshness of the beer, as well as their likelihood of purchase, and their estimate of the price. All of the beer came from the same batch (from Barney’s Brewery in Edinburgh). Nevertheless, those who evaluated the bottled beer rated it as tasting better than those who rated the beer that had been served from a can. Having demonstrated such a perceptual difference in terms of taste, we then went on to investigate whether people would prefer one packaging format over the other when the beer from bottle and can was served to a new group of participants blind (i.e., when the participants did not know the packaging material). The participants in this control study (N = 29) were asked which beer they preferred or else could state that the two samples tasted the same. No sign of preference was obtained under such conditions. Explanations for the psychological impact of the packaging format, in terms of differences in packaging weight (between tin and glass), and/or prior associations of quality with specific packaging materials/formats (what some have chosen to call ‘image molds’) are discussed.

The land use land cover (LULC) map is extensively employed for different purposes. The LULC maps provided by the Portuguese National Geographic Information System (SNIG) are not ideal for continuous assessment and regular scrutiny. Machine learning (ML) algorithms applied in remote sensing (RS) data has been proven effective in image classification, object detection, and semantic segmentation. Previous studies have shown that Random Forest (RF) and Support Vector Machine (SVM) consistently achieved high accuracy for land classification. In this study, the application of RF and SVM classifiers, and object-based (OBIA) and pixel-based (PBIA) approaches with Sentinel-2A imagery was evaluated using Google Earth Engine (GEE) platform for land cover classification of a burned area in the Serra da Estrela Natural Park (PNSE), Portugal. This aimed to detect the land cover change and closely observe the burned area and vegetation recovery after the 2022 wildfire. The combination of RF and OBIA achieved the highest accuracy in all evaluation metrics. At the same time, a comparison with the Normalized Difference Vegetation Index (NDVI) map and Conjunctural Land Occupation Map (COSc) of 2023 year indicated that the SVM and PBIA map resembled the maps better.

UAV network operation enables gathering and fusion from disparate information sources for flight control in both manned and unmanned platforms. In this investigation, a novel procedure for detecting runways and horizons as well as enhancing surrounding terrain is introduced based on fusion of enhanced vision system (EVS) and synthetic vision system (SVS) images. EVS and SVS image fusion has yet to be implemented real-world situations due to signal misalignment. We address this through a registration step to align the EVS and SVS images. Four fusion rules combining discrete wavelet transform (DWT) sub-bands are formulated, implemented and evaluated. The resulting procedure is tested on real EVS-SVS image pairs and pairs containing simulated turbulence. Evaluations reveal that runways and horizons can be detected accurately even in poor visibility. Furthermore, it is demonstrated that different aspects of the EVS and SVS images can be emphasized by using different DWT fusion rules. The procedure is autonomous throughout landing, irrespective of weather. We believe the fusion architecture developed holds promise for incorporation into head-up displays (HUDs) and UAV remote displays to assist pilots landing aircraft in poor lighting and varying weather. The algorithm also provided a basis rule selection in other signal fusion applications.

Magnetic resonance (MR) image acquisitions are usually time-consuming, limiting utilization in neuroimaging. For multiple sclerosis (MS) patients, MR imaging plays a major role in drug therapy decision-making. The purpose of this study was to evaluate whether an ultrafast, T2-weighted (T2w), deep-learning-enhanced (DL), echo-planar-imaging-based (EPI) fluid-attenuated inversion recovery (FLAIR) sequence (FLAIRUF) that has been targeting neurological emergencies so far might even be an option to detect MS lesions of the brain compared to conventional FLAIR sequences. Therefore, 17 MS patients were enrolled prospectively in this exploratory study. Standard MR protocols and ultrafast acquisitions were conducted at 3 tesla (T), including three-dimensional (3D)-FLAIR, turbo/fast spin echo (TSE)-FLAIR, and FLAIRUF. Inflammatory lesions were grouped by size and location. Lesion conspicuity and image quality were rated on an ordinal five-point Likert scale, and lesion detection rates were calculated. Statistical analyses were performed to compare results. Altogether, 568 different lesions were found. Data indicated no significant differences in lesion detection (sensitivity and positive predictive value [PPV]) between FLAIRUF and axially reconstructed 3D-FLAIR (lesion size ≥ 3 mm × ≥ 2 mm) and no differences in sensitivity between FLAIRUF and TSE-FLAIR (lesion size ≥ 3 mm total). Lesion conspicuity in FLAIRUF was similar in all brain regions except for superior conspicuity in the occipital lobe and inferior conspicuity in central brain regions. Further findings include location-dependent limitations of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) as well as artifacts such as spatial distortions in FLAIRUF. In conclusion, FLAIRUF could potentially be an expedient alternative to conventional methods for brain imaging in MS patients since the acquisition can be performed in a fraction of time while maintaining good image quality.

In Indonesia, types of meat are still identified manually due to increasing prices for beef and buf-falo ahead of Idul Fitri. Traders mix beef with pork to thwart the beef scam. Haar wavelet and GLCM, GLCM with angles of 0°, 45°, 90°, and 135°, as well as matrices using contrast, correlation, energy, homogeneity, and entropy, are used in feature extraction. The following are the results of meat image classification testing using k-NN, Haar wavelet, and GLCM: The k-NN algorithm shows superiority in identifying fresh (99%), frozen (99%), and rotten (96%) meat texture images with the most fantastic accuracy results in every situation. Meanwhile, GLCM routinely provides good results, especially regarding the texture image of fresh meat (183.21) and rotten meat (115.79). However, despite delivering less throughput than k-NN and GLCM, Haar wavelets are still helpful, especially when dealing with fresh meat texture images (89,96).

Drone and aerial remote sensing images are widely used, but their imaging environment is complex and prone to image blurring. Existing CNN deblurring algorithms usually use multi-scale fusion to extract features in order to make full use of aerial remote sensing blurred image information, but images with different degrees of blurring use the same weights, leading to increasing errors in the feature fusion process layer by layer. Based on the physical properties of image blurring, this paper proposes an adaptive multi-scale fusion blind deblurred generative adversarial network (AMD-GAN), which innovatively applies the degree of image blurring to guide the adjustment of the weights of multi-scale fusion, effectively suppressing the errors in the multi-scale fusion process and enhancing the interpretability of the feature layer. The research work in this paper reveals the necessity and effectiveness of a priori information on image blurring levels in image deblurring tasks. By studying and exploring the image blurring levels, the network model focuses more on the basic physical features of image blurring. Meanwhile, this paper proposes an image blurring degree description model, which can effectively represent the blurring degree of aerial remote sensing images. The comparison experiments show that the algorithm in this paper can effectively recover images with different degrees of blur, obtain high-quality images with clear texture details, outperform the comparison algorithm in both qualitative and quantitative evaluation, and can effectively improve the object detection performance of aerial remote sensing blurred images. Moreover, the average PSNR of this paper's algorithm tested on the publicly available dataset RealBlur-R reached 41.02dB, surpassing the latest SOTA algorithm.

Understanding plant species and community status is the most fundamental step in assessing biodiversity and considering environmental conservation, and these depend on the identification of plant species. Plant identification is usually done manually using botanical illustrations, but due to the diversity of plant species, identifying a single species from among many candidates listed in illustrated books is a labor-intensive and time-consuming task, even for experts. Against this background, systems for automatic plant species recognition are in demand by experts and amateurs alike. In this study, we proposed a method for automatic plant identification by applying multifractal analysis to images of leaf veins, including veinlets. In the experiment, we conducted two experiments: one to see if there is a change in the number of dimensions depending on the degree of leaf growth, and the other to see how the number of dimensions changes depending on the taxonomic group. As a result, there was almost no change depending on the degree of growth, and the number of dimensions varied depending on the taxonomic group, suggesting that different taxonomic groups can be classified into different taxonomic groups, suggesting that multifractal analysis can be applied to leaf vein images, including veinlets, to enable automatic plant identification.

Images are widely used in educational materials due to their ability to effectively convey complex concepts. However, unauthorized image use often leads to legal issues related to copyright infringement. To address this issue, we introduce a dual-module system designed specifically for educators. The first module, a copyright infringement detection system, leverages deep learning techniques to verify the copyright status of images. It employs a Convolutional Variational Autoencoder (CVAE) model to extract meaningful features from copyrighted images and compares them against user-provided images. If infringement is suspected, the second module, an image retrieval system, recommends alternative copyright-free images using a Vision Transformer (ViT)-based hashing model. Evaluation on benchmark datasets demonstrates the system's effectiveness, achieving a mean Average Precision (mAP) of 0.812 on the Flickr25k dataset. Furthermore, a user study with 65 teachers indicates high satisfaction levels, particularly in addressing copyright concerns and ease of use. Our system significantly aids educators in creating educational materials compliant with copyright regulations.

Image processing is a computationally intensive task that can benefit significantly from parallel techniques. In this study, we evaluate the efficiency of image processing using parallel techniques compared to sequential techniques. We use Dask to parallelize the loading and processing of images and compare the execution times for various image processing techniques, including smoothing, edge detection, rotation, and thresholding. The results show that parallel processing significantly reduces execution time compared to sequential processing.

The integration of advanced image analysis using artificial intelligence (AI) is pivotal for the evolution of autonomous vehicles (AVs). This article provides a thorough review of the most significant datasets and the latest state-of-the-art AI solutions employed in image analysis for AVs. Datasets such as Cityscapes, NuScenes, and CARLA form the benchmarks for training and evaluating different AI models, with unique characteristics catering to various aspects of autonomous driving. Key AI methodologies, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer models, and Generative Adversarial Networks (GANs), are discussed. The article also presents a comparative analysis of various AI techniques in real-world scenarios, focusing on semantic image segmentation, 3D object detection, and vehicle control in virtual environments. Simultaneously, the role of multisensor datasets and simulation platforms like AirSim, TORCS, and SUMMIT in enriching the training data and testing environments for AVs is highlighted. By synthesizing information on datasets, AI solutions, and comparative performance evaluations, the article serves as a crucial resource for researchers, developers, and industry stakeholders. Offering a clear view of the current landscape and future directions in autonomous vehicle image analysis technologies.

Weather conditions appear to be undergoing significant deviations from the long-term average, marked by pronounced extremes of heat, prolonged droughts, and heightened rainfall occurring with greater frequency worldwide. Consequently, new patterns of extreme weather are emerging, like the unusual and unorganized tropical cyclone called "Yaku", that influenced the extreme amount of rain that between March 6th to 10th, 2023 hits more than 1000 districts in the northwestern of Peru. One of the district more affected was Íllimo, in the Lambayeque province, due to the river that pass through the city, so called “La Leche”, after the continuous and intensive rain overflow devastating his surrounding areas and causing victims and damage. This emergency provided and excellent opportunity to apply, on the aforementioned areas, the “change detection” technique, that allows identifying areas of change between data obtained before and after of emergency events. In this research, optical data (Sentinel-2, Landsat, MODIS Terra and Aqua, and PeruSAT-1) and synthetic aperture radar (SAR) data (Sentinel-1 and COSMO SkyMed) were analyzed. A benchmark could also be carried out between open-source and commercial spatial analysis tools. The results indicated that in an extreme rain event the optical data are obscured by clouds and do not allow their use, while SAR data overcome clouds, and can be used for this research. The benchmark indicated that using the Jaccard index score, the Sentinel-1A set showed a 33% of correlation, while COSMO SkyMed demonstrated a 38% of match between the maps. The results obtained could be used to sensitize the population and develop a better management of future emergency events (floods) in different areas of Peru.

Ocean exploration is crucial for utilizing its extensive resources. Images captured by underwater robots suffer from issues such as color distortion and reduced contrast. To address the issue, we propose an innovative enhancement algorithm that integrates Transformer and Convolutional Neural Network (CNN) in a parallel fusion manner. Firstly, a novel transformer model is intro-duced to capture local features, employing peak-signal-to-noise ratio (PSNR) attention and linear operations. Subsequently, to extract global features, both temporal and frequency domain features are incorporated to construct convolutional neural network. Finally, the Fourier’s high and low-frequency information of the original image are utilized to fuse different features. To demon-strate the algorithm's effectiveness, underwater images with various levels of color distortion are selected for both qualitative and quantitative analyses. The experimental results demonstrate that our approach surpasses other mainstream methods, achieving superior PSNR and structural sim-ilarity index measure (SSIM) metrics and leading to a detection performance improvement of over ten percent.

This paper presents a repository of image databases where the image features are augmented with embedded vector field (VF) features to enhance the machine learning (ML) and increase classification statistics. Six VFs are formulated and used for the purpose of embedding. Three of them generate real shaped singular points (SPs): springing, sinking and saddle. The other three VFs generate seven kinds of SPs which include the real shaped SPs and four complex shaped SPs: repealing and attracting (out and in) spirals; clockwise and counterclockwise orbits (center). Also, the paper describes the VF singularities, their shapes, and locations according to the image objects. Then, the present work defines the mappings between the SPs’ shapes if the VFs are separately embedded into the same image. Thus, the paper infers which VFs are most appropriate for embedding into a particular image database such that the augmented SPs shapes enhance the ML classification. Examples of images with different embedded VFs are shown, in the text, to support and validate the theoretical conclusions. The contributions of the paper are: a description of the SPs location in an image; the mappings between the SPs of the different VFs; the definition of an imprint of an image in a VF; the generation of databases’ imprints. The advantage of classifying an image database with embedded VFs is that the new database enhances ML and improves the classification statistics if compared to the classification of the original image database with the same ML classifier. Furthermore, images with embedded SPs create a useful augmentation to the original image databases.

The accurate counting of passengers in public transport systems is crucial for optimizing operations, improving service quality, and planning infrastructure. It can also contribute to reducing the number of public transport lines where a high frequency of vehicles is not needed in certain periods during the year, but also by increasing the number of lines where the need is increased. This paper provides a comprehensive review of current methodologies and technologies used for passenger counting, with a focus on image processing and machine learning techniques and concepts. The paper explores various algorithms, including traditional computer vision methods and advanced deep learning approaches, highlighting their strengths and limitations. Additionally, the paper examines the integration of these technologies with other sensing modalities such as infrared sensors, Wi-Fi, and RFID. By analyzing recent advancements and case studies, this review aims to offer insights into the effectiveness, scalability, and practicality of different passenger counting solutions and offers a solution proposal. The paper also analyzed the current GDPR regulation that applies to the European Union and how it affects the use of systems like this. Future research directions and potential areas for technological innovation are also discussed to guide further developments in this field.

This paper introduces a novel framework for zero-shot learning (ZSL), i.e., to recognize new categories that are unseen during training, by distilling knowledge from foundation models. Specifically, we first employ ChatGPT and DALL-E to synthesize reference images of unseen categories from text prompts. Then, the test image is aligned with text and reference images using CLIP and DINO. Finally, the predicted logits are aggregated according to their confidence to produce the final prediction.Experiments are conducted on multiple datasets, including CIFAR-10, CIFAR-100, and TinyImageNet. The results demonstrate that our model can significantly improve classification accuracy compared to previous approaches, achieving AUROC scores above 96\% across all test datasets. Our code is available at https://github.com/1134112149/MICW-ZIC.

Image recovery by dictionary learning is of potential interest for many possible applications. To learn a dictionary, one needs to solve a minimization problem where the solution should be sparse. The K-SVD formalism, which is a generalization of the K-means algorithm, is one of the most popular methods to achieve this aim. We explain the preprocessing that is needed to bring images into a manageable format for the optimization problem.The learning process then takes place in terms of solving for sparse representations of the image batches. The main contribution of this paper is to give an experimental analysis of the recovery for highly textured imagery. For our study, we employ a subset of the Brodatz database. We show that the recovery of sharp edges plays a considerable role. Additionally, we study the effects of varying the number dictionary elements for that purpose.

Convolutional Neural Networks (CNNs) present drawbacks for modeling geometric transformations, such as scaling and rotation, caused by the convolution operation’s locality. Deformable convolution (DCON), a mechanism that substitutes standard convolution, increasing the receptive field to capture relevant features, is a promising approach to solve this drawback and improve the robustness of CNNs. However, the optimal way to replace the standard convolution with its deformable counterpart in a CNN model is unclear. In this study, we clarify this aseveration by conducting several experiments using deformable convolutions applied in the layers that conform a small four-layer CNN model. We also use deformable convolutions on the four-layers of several ResNet CNNs with depths 18, 34, 50, and 101. The models were tested in binary balanced classes with 2D data for image classification: Cats &amp; Dogs, EyesPACS, Spyders &amp; Chickens, and Shapes. After this testing, we evaluated DCON in 3D data for action recognition: UCF101 and Human2 (a dataset we compiled to control movement, clothing and background). The contribution of this research lies in a guideline to use DCON. It can be summarized as follows: if DCON is used on the first layers of the proposal of model (with simple features), the computational resources expressed as the quantity of Flops will tend to increase and produce bigger misclassification than the standard CNN. However, if the DCON is used at the end layers, the quantity of Flops used in the training and testing will decrease, and the classification accuracy will improve by up to 20% about the base model. Moreover, it gains robustness when using deformable convolutions because it can adapt to the region of interest. Also, the best kernel size of the DCON is three. It showed better results than size five. In the last case, the quantity of Flops increase quadratically, but their performance does not increase significantly. With these results, we propose a guideline to use the DCON and contribute to understanding the impact of DCON on the robustness of CNNs.

In this paper, we introduce a specialized, fully automated experimental arrangement designed for assessing the effectiveness of augmented and virtual reality technologies in healthcare setting and explore its design principles focusing on depth sensor characterization. The main goal of this project is to establish a robust benchtop platform for the quantitative evaluation of extended reality technologies in a controlled environment, emphasizing the practical assessment of augmented reality head-mounted devices. We outline a design concept and considerations for an experimental configuration aimed at creating a realistic testing environment. This apparatus comprises an observation platform mounted on a three-degree-of-freedom motorized system and a test phantom stage. Focusing on real-case scenarios, we utilized a range of sensors, including readily available range-sensing cameras and commercial augmented reality headsets, such as the Intel RealSense L515 LiDAR camera, mounted on the motion control system. The setup enabled automated data collection and facilitates controlled assessments and validation of various facets of augmented reality technologies, including object, hand, and head tracking, as well as static and dynamic image registration. This paper describes the system architecture, the process of automated data collection, and presents several evaluation studies conducted with this setup, providing insights into the quality and temporal noise associated with static or moving objects or observers in augmented reality applications.

High-power laser facilities necessitate predicting incremental damage to final optics to identify evolving damage trends. In this study, we propose a surface damage detection method utilizing image segmentation employing ResNet-18, and a damage area estimation network employing U-Net++. Paired sets of online and offline images of optics obtained from a large laser facility is used to train the network. The trends of varying damage could be identified by incorporating additional experimental parameters. A key advantage of the proposed method is that the network can be trained end-to-end on small samples, eliminating the need for manual labeling or feature extraction. The software developed based on these models can facilitate the daily inspection and maintenance of optics in large laser facilities.

Open-vocabulary learning has recently gained prominence as a means to enable image segmentation for arbitrary categories based on textual descriptions. This advancement has extended the applicability of segmentation systems to a broader range of generally purpose scenarios. However, current methods often revolve around specialized architectures and parameters tailored to specific segmentation tasks, resulting in a fragmented landscape of segmentation models. In response to these challenges, we introduce OVAMTSeg, a versatile framework designed for Open-Vocabulary and Multitask Image Segmentation. OVAMTSeg harnesses adaptive prompt learning to empower the model to capture category-sensitive concepts, enhancing its robustness across diverse multi-task and scenario contexts. Text prompts are employed to effectively capture semantic and contextual features of the text, while cross-attention and cross-modal interactions enable the fusion of image and text features. Furthermore, a transformer-based decoder is incorporated for dense prediction. Extensive experimental results underscore the effectiveness of OVAMTSeg, showcasing its state-of-the-art performance and superior generalization capabilities across three segmentation tasks. Notable achievements include a 47.5 mIoU in referring expression segmentation, 51.6 mIoU on Pascal-VOC with four unseen classes, 46.6 mIoU on Pascal-Context in zero-shot segmentation, 65.9 mIoU on Pascal-5i, and 35.7 mIoU on COCO-20i datasets for one-shot segmentation.

"Deep fake" technologies are often associated with face-swapping algorithms based on images or videos. Although much research is focused on developing these techniques, there is still room for improvement, especially in human and visual assessment. In this study, we present an innovative approach to the problem of face replacement based on one model while preserving background features. Our model uses the Adaptive Attentional Denormalization method from FaceShifter while integrating identity features from the ArcFace and BiSeNet models to support the attribute extraction process. We used Fast GAN as a generative model to optimize and accelerate the training process on smaller data sets. Our research proves the effectiveness of the proposed approach by combining innovative methods and architectures in one coherent solution.

Traditional image steganography conceals secret messages into unprocessed natural images by modifying the pixel value, causing the obtained stego different from the original image in terms of statistical distribution, thereby could be detected by a well-trained classifier for steganalysis. To ensure the steganography is imperceptible and in line with the trend of art images produced by Artificial Intelligence Generated Content (AIGC) becoming popular in social networks, this paper proposes to embed hidden information throughout the process of the generation of an art-style image by designing an image style transformation neural network with steganography function. The proposed scheme takes a content image, an art-style image, and messages to be embedded as inputs, processing them with an encoder-decoder model, and finally generates a styled image containing the secret messages at the same time. An adversarial training technique is applied to enhance the imperceptibility of the generated art-styled stego image with from plain style-transferred images. The lack of the original cover image makes it difficult for the opponent learning a steganalyzer to identify the stego. The recommended approach can successfully withstand existing steganalysis techniques and attain the embedding capacity of 3 bits per pixel for a color image, according to experimental results.

The increased number of buildings in urban areas limits the creation of vegetated areas, leading to the search for alternatives to create spaces to promote contact with nature. In this context, green roofs have been increasingly studied. These structures have specific microclimatic condi-tions requiring an accurate study of the most appropriate vegetation to use. This study aims to analyze the long-term viability of vegetation installed on an experimental green roof open-air lab. This analysis was done through images obtained from photographic records and later in-serted into the ImageJ program, to identify species and evaluate the area covered by vegetation. Only a few of the species that were planted in the test beds over the years have persisted to the present, while other species have spontaneously appeared. Also, surveys were used to learn about people's preferences for the vegetation on these test beds. These showed that people favor recognizable plants with plenty of vibrantly colored blossoms. It was feasible to choose the best plants for green roofs in the studied conditions as a result of the analysis, taking into account the ground cover percentage by vegetation, its persistence, and the preferences of the respondents.

Cervical cancer remains a major public health problem. Despite the screening programs, cervical cancer is the second most commonly diagnosed cancer. Most of the cases occur in less developed countries, and it is the third leading cause of cancer death among females. In 2000–2007, the five-year relative survival rate for European women diagnosed with cervical cancer was 62%.[1] Standard treatment for locally advanced cervical cancer reveals external beam radiotherapy (EBRT) with concurrent chemotherapy followed by image-guided adaptive brachytherapy (IGABT) boost [2, 3]. In the last years, the Gynaecological (GYN) working group composed of Group Européen de Curiethérapie-European Society for Therapeutic Radiology and Oncology (GEC-ESTRO) has developed new recommendations for the treatment of cervical cancer based on IGABT. The treatment plan includes 3-dimensional (3D) imaging based on magnetic resonance imaging (MRI) or computed tomography (CT), adaptive to the target volume and organ at risk [4–6]. The brachytherapy insertion is made under anaesthesia. At the time of the application insertion, the ultrasound is used to guide the dilatation of the uterine canal and assess the depth of the uterine cavity. In this paper, we review the data available in international literature about the use of ultrasound (US) in brachytherapy treatment of cervical cancer.

Machine learning models facilitate the search for non-linear relationships when modeling hydrological processes, but they are equally effective for automation at the data preparation stage. The tasks whose automation was analyzed consisted in estimating changes in the roughness of a mountain streambed and the extent of floods from images. The Segment Anything Model (SAM) developed this year by Meta was used for this purpose. Images from many years from the Wielka Puszcza mountain stream located in the Polish Carpathians were used as the only input data. The model was not additionally trained for the described tasks. The model can be run in several modes, but the two most appropriate were used in this study. The first one is available in the form of a web application, while the second one is available in the form of a Jupyter notebook run in the Google Colab environment. Both methods do not require specialized knowledge and can be used by virtually any hydrologist. In the roughness estimation task, the average Intersection over Union (IoU) ranges from 0.55 for grass to 0.82 for shrubs/trees. Ultimately, it was possible to estimate the roughness coefficient of the mountain streambed between 0.027 and 0.059 based solely on image data. In the task of estimation of the flood extent, when selecting appropriate images, one can expect IoU at the level of at least 0.94, which seems to be an excellent result considering that SAM is a general-purpose segmentation model. It can therefore be concluded that SAM model can be a useful tool for a hydrologist.

Body image disturbance (BID) involves negative attitudes towards shape and weight and is associated with lower levels of interoception (awareness of and attention toward internal physiological states). This association is considered a risk factor for developing eating disorders (EDs) and is linked to altered sensorimotor representations of the body (i.e., body schema). BIDs manifest across genders and are currently understudied in men. This study investigated gender-related differences in BID and its relationship to the body schema and interoception. Data were collected from 86 men and women. BID was assessed using questionnaires measuring self-objectification, state, and trait body dissatisfaction. Interoceptive sensibility was measured via the MAIA-2. The body schema was indexed via an embodied mental rotation task. Results showed that women reported higher BID than men across all scales. Gender differences in sub-components of interoceptive sensibility were found. Overall, both gender and interoceptive sensibility predicted BID. However, interoceptive sensibility exhibited its own unique association with BID beyond the influence of gender. BID, IS and gender were not significant predictors of performance in the body schema task. Therefore, while gender predicts differences in BID and interoceptive sensibility, there was no evidence of gender-related differences in body schema.

Recently, Artificial Intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapies as well as increasing the effectiveness of the training process. Thus, AI may contribute to the automatization of the image scan segmentation process and increase the quality of the resulting 3D objects, which may lead to the generation of more realistic virtual objects. In this paper, we focus on the AI-based solutions applied in the medical image scan segmentation, and intelligent visual-content generation, i.e. computer-generated three-dimensional (3D) images in the context of Extended Reality (XR). We consider different types of neural networks used with a special emphasis on the learning rules applied, taking into account algorithm accuracy and performance, as well as open data availability. This paper also attempts to summarize the current development of AI-based segmentation methods in medical imaging and intelligent visual content generation that are applied in XR. Finally, this paper concludes with possible developments and open challenges in AI application in Extended Reality-based solutions. Finally, the future lines of research and development directions of Artificial Intelligence applications both in medical image segmentation and Extended reality-based medical solutions are discussed.

Background: Computed tomography examinations have produced high radiation doses to patients, especially the CT brain. This study aimed to optimize the radiation dose and image quality in adult CT brain protocol. Materials and Methods: Images were acquired by Catphan 700 phantom. Radiation doses were recorded as CTDIvol and dose length product (DLP). CT brain protocols were optimized by varying parameters such as kVp, mAs, signal-to-noise ratio (SNR) level, and Clearview iterative reconstruction (IR). Image quality were also evaluataion by The AutoQA Plus software. Results: CT Number accuracy and linearity had a robust positive correlation with the linear attenuation coefficient (µ) and showed more inaccurate CT numbers when using 80 kVp. MTF showed a higher value in 100 and 120 kVp protocols, while high contrast spatial resolution showed a higher value in 80 and 100 kVp protocols. Low contrast detectability and CNR tended to increase when using high mAs, SNR and Clearview IR protocol. Noise decreased when using a high radiation dose and a high percentage of Clearview IR. CTDIvol and DLP were increased with increasing kVp, mAs, and SNR levels, while the increasing percentage of Clearview did not affect the radiation dose. Conclusion: Optimized protocols, including radiation dose and image quality, should be evaluated to preserve diagnostic capability. The recommended parameter settings include kVp set between 100-120 kVp, mAs ranging from 200-300 mAs, SNR level within the range of 0.7-1.0, and an iterative reconstruction value of 30% Clearview to 60% or higher.

Background: Assessing body image and personalized rehabilitation in lower limb amputation is essential to measure impact on an individual’s quality of life and psychosocial wellbeing. The aim of this study was to develop a Spanish version of the Amputee Body Image Scale (ABIS-E) for Spanish-speaking lower limb amputee. Methods: A cross-sectional study was conducted including amputee patients in local in Madrid, Spain. Clinical outcomes about body image (Amputee Body Image Scale, ABIS), health-related quality of life (EuroQol-5D-5L), depression and anxiety (Hospital Anxiety and Depression Scale, HADS), pain (Brief Pain Inventory-Short Form, BPI-SF) and functional capacity (Prosthesis Evaluation Questionnaire, PEQ) were collected. Results: Seventy-three participants were assessed. Excellent test-retest reliability (ICC=0.847) and good internal consistency (Cronbach's alpha=0.753) were obtained. Three factors were extracted in the factor analyses. Convergent validity with Pearson's and Spearman's correlation coefficients were calculated for the depression and anxiety questionnaires (HADS) (values between .57 and .67), functional capacity (PEQ) (values between -.35 and .71) and quality of life (EQ-5D-5L) (values between -.37 and .61). Conclusions: The ABIS-E is a reliable and valid tool for measuring body image in the Spanish population with a lower limb amputation, potentially useful in primary care to identify psychosocial problems.

Camera malfunctions or loss of storage elements in imaging devices may lead to loss of important image information or random impulse noise interference. Low rank is one of the important prior information in image optimization processing. This paper uses different low-rank constraint models of the image matrices to recover the impulse interference satellite images. Firstly, an overview of image inpainting models based on nuclear norm, truncated nuclear norm, weighted nuclear norm, and matrix-factorization-based F-norm is presented, and corresponding optimiza-tion iterative algorithms are provided. Then, we conducted experiments under three types of pulse interference and provided visual and numerical results. Finally, it is concluded that the WSVT_ADMM method based on the weighted nuclear norm can obtain the best image inpainting results; The UV_ADMM method based on F norm of matrix factorization has the least computa-tion time and can be used for large-scale low-rank matrix computation; The WSVT_ADMM method based on weighted nuclear norm and the TSVT_ ADMM method based on truncated nuclear norms can significantly improve the repair effect compared to the nuclear norm-based methods such as SVT, SVP, and n_ADMM.

Image encryption based on chaotic maps is an important way to ensure the secure communication of digital multimedia on the Internet. To improve the encryption performance and security of image encryption systems, a new image encryption algorithm is proposed by employing a compound chaotic map and random cyclic shift. Firstly, an improved Hybrid chaotic system is designed by coupling Logistic, ICMIC, Tent, and Chebyshev maps (HLITC). Comparison tests with previous chaotic maps in terms of chaotic trajectory, Lyapunov exponent, and Kolmogorov entropy illustrate the new hybrid chaotic map has better chaotic performance. Then the proposed HLITC chaotic system is used to develop a new chaotic image encryption scheme with the double cyclic shift. The improved HLITC chaotic system is performed to generate key sequences used in the image scrambling and diffusion stage. The spiral transformation controlled by chaotic sequence is used to scramble the pixels of the plaintext image, while the XOR operation based on a chaotic map is used for pixel diffusion. Extensive experiments of statistical analysis, key sensitivity, and keyspace analysis are conducted. Experimental results show the proposed encryption scheme has good robustness against brute force attacks, statistical attacks, and differential attacks, and is more effective than many existing chaotic image encryption algorithms.

This work tackles the problem of image restoration, a crucial task in many fields of applied sciences, focusing on removing degradation caused by blur and noise during the acquisition process. Drawing inspiration from the multi-penalty approach based on the Uniform Penalty principle introduced in [Bortolotti et al. arXiv.math.NA/2309.14163], we develop here a new image restoration model and an iterative algorithm for its effective solution. The model incorporates pixel-wise regularization terms and establishes a rule for parameters selection, aiming to restore images through the solution of a sequence of constrained optimization problems. To achieve this, we present a modified version of the Newton Projection method, adapted to multi-penalty scenarios, and prove its convergence. Numerical experiments demonstrate the efficacy of the method in eliminating noise and blur while preserving the image edges.

Single image deraining (SID) has shown its importance in many advanced computer vision tasks. Though many CNN based image deraining methods have been proposed, how to effectively remove raindrops while maintaining background structure remains a challenge that needs to be overcome. Most of the deraining work focuses on removing rain streaks, but in heavy rain images, the dense accumulation of rainwater or the rain curtain effect significantly interferes with the effective removal of rain streaks, and often introduces some artifacts that make the scene more blurry. In this paper, we propose a new network structure R-PReNet for single image deraining with good background structure maintaining. This framework fully utilizes the cyclic recursive structure of PReNet. Moreover, we introduce residual channel prior (RCP) and feature fusion modules for better deraining performance by focusing on background feature information. Compared with the previous methods, our method has significantly improvement effect on the rainstorm image with the artifacts removing and good visual detail restoring.

This study proposes a method for image matching between infrared(IR)-RGB images using a deep learning network to estimate the deformation field. We propose a deformation field generator (DFG) that estimates the deformation field of the transformation matrix to match each pixel or IR image to the RGB image. DFG is a network that receives IR and RGB images as input; the output size is two channels and has the sample resolution as the input image. By warping the IR image through a grid-sampler that warps the image according to the value of the deformation field, we can obtain a warped IR image that matches the RGB image. Additionally, to check whether the warped IR image matched the RGB image, the masking images detecting the segmentation of objects were photographed in two images. Without directly comparing IR and RGB images, we proposed mask loss that warps the IR mask image through the deformation field and grid sampler and then compares the warped IR mask image with the RGB mask image. Mask loss solves the spatial similarity comparison problem with multi-modality images, such as IR and RGB images, by comparing the mask image with the same modality image as the mask image.

It is crucial to select the right machine learning classifier for image classification and face recog-nition. This study examines the effectiveness of four different face recognition classifiers - Support Vector Machines (SVM), Random Forest, K-Nearest Neighbors (KNN), and Neural Networks. An analysis of the Large Faces in the Wild (LFW) dataset was carried out using Principal Component Analysis (PCA). Classifiers are rigorously trained and evaluated based on the extracted features. Comparison of classifier performance is an insightful way to figure out their strengths and weaknesses. Having a visual representation of the classifier's performance gives a complete understanding of its capabilities. Through the selection of the most appropriate classifier, study results contribute to advancements in image classification, recognition, and biometric identification. The comparison study demonstrated that the Neural Network classifier was exceptionally accurate and proficient in recognizing faces from the LFW dataset when used in conjunction with PCA for feature extraction. According to the comparative analysis, the Neural Network classifier proved exceptionally accurate and proficient at identifying faces from the LFW dataset when combined with PCA for feature extraction.

Spinal deformity refers to a range of disorders that are defined by anomalous curvature of the spine and may be classified as scoliosis, lordosis, or kyphosis. Among these, scoliosis stands out as the most common type of spinal deformity in human beings, and it can be distinguished by abnormal lateral spine curvature accompanied by axial rotation. Accurate identification of spinal deformity is crucial for a person's diagnosis, and numerous assessment methods have been developed by researchers. Therefore, the present study aims to systematically review recent works on spinal deformity as-assessment for scoliosis diagnosis, utilizing image processing techniques. To gather relevant studies, a search strategy was conducted on three electronic databases (Scopus, ScienceDirect, and PubMed) between 2012 and 2022, using specific keywords and focusing on scoliosis cases. A total of 17 papers fully satisfied the established criteria and were extensively evaluated. Despite variations in methodological designs across the studies, all reviewed articles obtained quality ratings higher than satisfactory. Various diagnostic approaches have been employed, including artificial intelligence mechanisms, image processing, and scoliosis diagnosis systems. These approaches have the potential to save time and, more significantly, can reduce the incidence of human error. While all assessment methods have potential in scoliosis diagnosis, they possess several limitations that can be ameliorated in forthcoming studies. Therefore, the findings of this study may serve as guidelines for the development of a more accurate spinal deformity assessment method that can aid medical personnel in the real diagnosis of scoliosis.

(1) Background: Colon polyps are common protrusions in the colon’s lumen with potential risks of developing colorectal cancer. Early detection and intervention of these polyps are vital for reducing colorectal cancer incidence and mortality rates. This research aims to evaluate and compare the performance of three machine-learning image classification models’ performance in detecting and classifying colon polyps. (2) Methods: The performance of three machine learning image classification models, Google Teachable Machine (GTM), Roboflow3 (RF3), and You Only Look Once version 8 (YOLOV8), in the detection and classification of colon polyps were evaluated. The study used a dataset of colonoscopy images of normal colon, polyps, and resected polyps. The study assessed the models’ ability to correctly classify the images into their respective classes using precision, recall, and F1 score generated from confusion matrix analysis and performance graphs. (3) Results: All three models successfully distinguished between normal colon, polyps, and resected polyps in colonoscopy images. GTM achieved the highest accuracies: 0.99, with consistent precision, recall, and F1 scores of 1.00 for the `normal’ class, 0.97 - 1.00 for `polyps,’ and 0.97 - 1.00 for `resected polyps.’ While GTM exclusively classified images into these three categories, both YOLOV8 and RF3 extended their capabilities to detect normal colonic tissue, polyps, and resected polyps, with YOLOV8 and RF3 achieving overall accuracies of 0.84 and 0.87, respectively. (4) Conclusions: Machine learning, particularly models like GTM, shows promising results in ensuring comprehensive detection of polyps during colonoscopies.

Plant phenotype plays an important role in crop breeding and planting. Leaf phenotype is an important part of plant phenotype. In order to analyze the leaf phenotype, the target leaf is required to be segmented from the complex background image. In this paper, an automatic soybean leaf segmentation method based on object detection and interactive segmentation models is proposed. Firstly, the Libra R-CNN object detection algorithm is used to detect all soybean leaves in the image. Then, based on the idea that the target soybean leaf is located in the center of the image and the area is large, the detection bounding box of the target leaf is selected. In order not to destroy the segmentation result, the bounding box is optimized to completely enclose the whole leaf. Finally, according to the optimized bounding box, the prior channels of foreground and background are constructed using Gaussian model. The two channels together with the original image are as the input of the interactive object segmentation with inside-outside guidance model to segment the target soybean leaf. A large number of qualitative and quantitative experimental results show that the method has high segmentation accuracy and strong generalization capacity.

Fractures of the talar neck and body are associated with spine fractures and scoliosis deformity, which affect cosmetic appearance and cause difficulty in ambulation. The implant design for talus surgery is thriving as a functional alternative in case of severe talar destruction, focusing on segmentation and reconstruction of the talus’s shape. However, manual segmentation of the talus is time-consuming and subjective. In this study we exploited the automatic segmentation framework to efficiently train a deep learning-based model to accurately segment the talus based on computed tomography imaging. We developed three model configurations with nnU-Net and investigated their Dice similarity coefficients (DSC) and 95% Hausdorff distances (HD95) for talus segmentation on a CT image dataset. The three configurations performed well (DSC > 0.95, HD95 < 0.6). When tested on the same samples, one of the configurations was more efficient while ensuring higher accuracy. We propose to focus on talus anatomic variations with increasing age based on this framework and apply it to clinical trials at the next stage.

To solve the problem that the common long-tailed classification method does not use the semantic features of the original label text of the image, and the difference between the classification accuracy of most classes and minority classes is large, the long-tailed image classification method based on enhanced contrast visual language trains the head class and tail class samples separately, uses text image to pre-train the information, and uses enhanced momentum contrast loss function and RandAugment enhancement to improve the learning of tail class samples. On the ImageNet-LT long-tailed dataset, the enhanced contrastive visual-language based long-tailed image classification method has improved all class accuracy, tail class accuracy, middle class accuracy, and F1 values by 3.4%, 7.6%, 3.5%, and 11.2%, respectively, compared to the BALLAD method. The difference in accuracy between the head class and tail class is reduced by 1.6% compared to the BALLAD method. The results of three comparative experiments indicate that the long-tailed image classification method based on enhanced contrastive visual-language has improved the performance of tail classes and reduced the accuracy difference between majority and minority classes.

Crop disease classification has always been a critical and persistent problem in the field of agricultural and forestry sciences, where often we do not have access to a sufficient number of samples to know the distribution of real-world samples. How to make full use of the existing data is the starting point of our thinking. To address this problem, this paper proposes a supervised image augmentation method Negative Contrast, which uses the contrast images of existing disease samples after removing disease areas as negative samples for image augmentation when samples are relatively scarce. Numerous experiments have shown that several classical models using this augmentation method have improved in disease classification of four crops, rice, wheat, corn, and soybean, with a maximum accuracy improvement of 30.8%. In addition, the comparative analysis of attentional heat map shows that the model using Negative Contrast is more accurate and intense on the area of interest of diseases, and thus reflects better generalization ability in real-world disease classification. Our dataset and codes can be found in https://www.kaggle.com/datasets/w970704112/corn-wheat-rice-soybean and https://github.com/hiter0/contrastaug .

UAV has become powerful devices for both practitioners and researchers. In recent years, there has been a substantial growth in the usage of UAV for a wide range of applications in research and engineering. UAV are mostly utilized for image capturing techniques due to their time savings, low cost, minimum field work, and high precision. The purpose of this study is to generate an accurate and reliable 3D modelling process using UAVs photogrammetric technology; hence this study allows the techniques to evaluate the practicability of modelling and mapping inaccessible tunnels using images recorded by UAVs and photogrammetry. As a result, the approach was always employed to depict the true state of the place. This research incorporates three experiments. The first experiment is to build a 3D model using only one-directional light source and evaluate the accuracy of 3D photogrammetric model and conducted at the concrete tunnel. The second one is to build a 3D model of underground sewer tunnel using different light sources one-directional and omni-directional. The last experiment was to build 3D model using an UAV system which is developed based on first and second experiment’s result, evaluate an accuracy of 3D photogrammetric model and conducted at the tunnel named in the research as a “pedestrian tunnel. There is certain limitation in this study such as Adding a payload to a drone changed its weight distribution, which affected its stability. If the payload was not properly balanced, it caused the drone to fly unevenly, making it more difficult to control.

Alzheimer’s disease has become a major concern in the healthcare domain as it is growing rapidly. Many researches have been conducted to detect it from MRI images through various deep-learning approaches. However, the problem of the availability of medical data and the privacy of the patients still exists. To mitigate this issue in Alzheimer’s disease detection, we have implemented the federated approach which is found to be more efficient, robust, and consistent compared to the conventional approach. For this, we needed deep excavation on various orientations of MRI images and transfer learning architectures. Then, we utilized the publicly available two datasets (OASIS, and ADNI), and designed various cases to evaluate the performance of the federated approach. In almost all of them, the federated approach achieved better accuracy and sensitivity. In distinguishing between two classes equally, the capability of the models was better in the found model when trained with federated approach compared to the conventional approach. In this approach. MobileNet, which is a low-cost transfer learning architecture, achieved the highest 95.24%, 81.94%, and 83.97% accuracy in OASIS, ADNI, and merged (ADNI+OASIS) test set, which was way higher than the achieved performance in Conventional approach. Moreover, in this approach, only the weights of the model were shared which keeps the original MRI images in their respective hospital or institutions, mitigating the privacy concern in the healthcare domain.

Recent advances in deep learning technology have led to increased interest in text-to-image technology, which enables computers to create images from text by simulating the human process of forming mental images. The GAN-based text-to-image technology involves the extraction of features from input text, which are combined with noise and then used as input to a GAN that generates images that are similar to the original images through competition between the generator and discriminator. Although generating images from English text is a mature area of research, text-to-image technology based on multilingualism, such as Korean, is still in its early stages of development. Webtoon is a digital comic format that allows comics to be viewed online. The creation process for webtoons is divided into story planning, content/sketching, coloring, and background drawing. Since each stage of webtoon production requires human intervention, it is both time-consuming and expensive. As a result, deep learning technologies such as automatic coloring and automatic line drawing are being used to reduce human involvement. However, there is a shortage of technology that can assist authors with story creation in webtoon production. Therefore, this study proposes a multilingual text-to-image model capable of generating webtoon images when presented with multilingual input text. The proposed model employs Multilingual BERT to extract feature vectors for multiple languages, and trains a DCGAN in conjunction with the images. The experimental results demonstrate that the model can generate images that are similar to the original images when presented with multilingual input text after training.