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TFR: Texture Defect Detection with Fourier Transform using Normal Reconstructed Template of Simple Autoencoder (2307.04574v1)

Published 10 Jul 2023 in cs.CV and eess.IV

Abstract: Texture is an essential information in image representation, capturing patterns and structures. As a result, texture plays a crucial role in the manufacturing industry and is extensively studied in the fields of computer vision and pattern recognition. However, real-world textures are susceptible to defects, which can degrade 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 are 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, since texture defects often exhibit characteristic changes in specific frequency ranges, analyzing the frequency domain enables effective defect detection. The proposed method demonstrates effectiveness and accuracy in detecting texture defects. Experimental results are presented to evaluate its performance and compare it with existing approaches.

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References (20)
  1. "AnoGAN: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery," In European Conference on Computer Vision, pp. 823-834, 2016.
  2. "Unsupervised anomaly detection with generative adversarial networks to guide marker discovery," In International Conference on Information Processing in Medical Imaging, pp. 146-157, 2017.
  3. "GANomaly: Semi-supervised anomaly detection via adversarial training," In Asian Conference on Computer Vision, pp. 622-637, 2018.
  4. "Skip-GANomaly: Skip connected and adversarially trained encoder-decoder anomaly detection," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 39-42, 2018.
  5. "Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection," In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1705-1714, 2019.
  6. "OCGAN: One-class novelty detection using GANs with constrained latent representations," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2898-2906, 2019.
  7. "Plant disease detection using generated leaves based on DoubleGAN," IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 19, No. 3, pp. 1817-1826, 2021.
  8. "Chili Pepper Disease Diagnosis via Image Reconstruction Using GrabCut and Generative Adversarial Serial Autoencoder," arXiv preprint arXiv:2306.12057, pp. 1-12, 2023.
  9. "Traffic Accident Detection in First-Person Videos Based on Depth and Background Motion Estimation," Journal of Korean Institute of Information Technology (JKIIT), Vol. 19, No. 3, pp. 25-34, 2021.
  10. "Defect detection in electronic surfaces using template-based Fourier image reconstruction," IEEE Transactions on Components, Packaging and Manufacturing Technology, Vol. 9, No. 1, pp. 163-172, 2018.
  11. "Surface Anomaly Detection of Wood Grain Image Using Fourier Transform: A Preliminary Study," In Proceedings of Korean Institute of Information Technology Conference, pp. 86-87, 2022.
  12. "Draem: A discriminatively trained reconstruction embedding for surface anomaly detection," In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8330-8339, 2021.
  13. "Omni-frequency channel-selection representations for unsupervised anomaly detection," arXiv preprint arXiv:2203.00259, pp. 1-14, 2023.
  14. "N-pad: Neighboring pixel-based industrial anomaly detection," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4364-4373, 2023.
  15. "Difference Image Alignment Technique of Reconstruction Method for Detecting Defects in Thermal Image of Solar Cells," Journal of Korean Institute of Information Technology (JKIIT), Vol. 21, No. 5, pp. 11-19, 2023.
  16. "Multi-scale patch-based representation learning for image anomaly detection and segmentation," In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 3992-4000, 2022.
  17. "Reconstruction from edge image combined with color and gradient difference for industrial surface anomaly detection," arXiv preprint arXiv:2210.14485, pp. 1-11, 2022.
  18. "Unsupervised anomaly segmentation via deep feature reconstruction," Neurocomputing, Vol. 424, pp. 9-22, 2021.
  19. "Divide-and-assemble: Learning block-wise memory for unsupervised anomaly detection," In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 8791-8800, 2021.
  20. "MVTec AD–A comprehensive real-world dataset for unsupervised anomaly detection," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9592-9600, 2019.

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