TFR：使用简单自编码器的正常重建模板结合傅里叶变换进行纹理缺陷检测
TFR: Texture Defect Detection with Fourier Transform using Normal Reconstructed Template of Simple Autoencoder
AI总结：
本文提出TFR方法，将简单自编码器重建的正常模板与傅里叶变换频域分析相结合，用于纹理缺陷检测，并通过实验验证其有效性和准确性。
摘要：
纹理是图像表示中的一种基本信息，能够捕捉模式和结构。因此，纹理在制造业中起着至关重要的作用，并在计算机视觉和模式识别领域得到广泛研究。然而，现实世界中的纹理容易受到缺陷的影响，这会降低图像质量并引发各种问题。因此，需要准确且有效的方法来检测纹理缺陷。本研究采用简单自编码器和傅里叶变换进行纹理缺陷检测。所提出的方法将傅里叶变换分析与从简单自编码器获得的重建模板相结合。傅里叶变换是分析图像和信号频域的强大工具。此外，由于纹理缺陷通常在特定频率范围内表现出特征性变化，分析频域能够实现有效的缺陷检测。所提出的方法在检测纹理缺陷方面表现出有效性和准确性。文中给出了实验结果，以评估其性能并与现有方法进行比较。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
