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TFR: Texture Defect Detection with Fourier Transform using Normal Reconstructed Template of Simple Autoencoder

arXiv 2307.04574 ↗First posted 10 Jul 2023cs.CV eess.IVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 19 references that are in the map. First posted on arXiv 10 Jul 2023.

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References · 11

  1. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  2. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 560
  3. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 510
  4. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  5. 2019OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representationscited by 199
  6. 2019Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detectioncited by 100
  7. 2021Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detectioncited by 83
  8. 2022Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detectioncited by 79
  9. 2022Reconstruction from edge image combined with color and gradient difference for industrial surface anomaly detectioncited by 18
  10. 2022N-pad : Neighboring Pixel-based Industrial Anomaly Detectioncited by 11
  11. 2023Chili Pepper Disease Diagnosis via Image Reconstruction Using GrabCut and Generative Adversarial Serial Autoencodercited by 1

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