Version 1
: Received: 23 September 2023 / Approved: 25 September 2023 / Online: 25 September 2023 (11:23:30 CEST)
How to cite:
Si, J.; Kim, S. TFR: Texture Defect Detection with Fourier Transform Using Normal Reconstructed Template of Simple Autoencoder. Preprints2023, 2023091666. https://doi.org/10.20944/preprints202309.1666.v1
Si, J.; Kim, S. TFR: Texture Defect Detection with Fourier Transform Using Normal Reconstructed Template of Simple Autoencoder. Preprints 2023, 2023091666. https://doi.org/10.20944/preprints202309.1666.v1
Si, J.; Kim, S. TFR: Texture Defect Detection with Fourier Transform Using Normal Reconstructed Template of Simple Autoencoder. Preprints2023, 2023091666. https://doi.org/10.20944/preprints202309.1666.v1
APA Style
Si, J., & Kim, S. (2023). TFR: Texture Defect Detection with Fourier Transform Using Normal Reconstructed Template of Simple Autoencoder. Preprints. https://doi.org/10.20944/preprints202309.1666.v1
Chicago/Turabian Style
Si, J. and Sungyoung Kim. 2023 "TFR: Texture Defect Detection with Fourier Transform Using Normal Reconstructed Template of Simple Autoencoder" Preprints. https://doi.org/10.20944/preprints202309.1666.v1
Abstract
Texture is essential information for image representation, capturing patterns, and structures. Consequently, texture plays a crucial role in the manufacturing industry and has been extensively studied in the fields of computer vision and pattern recognition. However, real-world textures are susceptible to defects, which can degrade the image quality and cause various issues. Therefore, there is a need for accurate and effective methods to detect texture defects. In this study, a simple autoencoder and Fourier transform were employed for texture defect detection. The proposed method combines Fourier transform analysis with the reconstructed template obtained from the simple autoencoder. Fourier transform is a powerful tool for analyzing the frequency domain of images and signals. Moreover, analyzing the frequency domain enables effective defect detection because texture defects often exhibit characteristic changes in specific frequency ranges. The proposed method demonstrates effectiveness and accuracy in detecting texture defects. Experimental results are presented to evaluate its performance and compare it with those of existing approaches.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
