Automated surface inspection for statistical textures

In this paper we present a global approach for the automatic inspection of defects in randomly textured surfaces which arise in sandpaper, castings, leather, and many industrial materials. The proposed method does not rely on local features of textures. It is based on a global image reconstruction scheme using the Fourier transform. Since a statistical texture has the surface of random pattern, the spread of frequency components in the power spectrum space is isotropic and forms the shape approximate to a circle. By finding an adequate radius in the spectrum space, and setting the frequency components outside the selected circle to zero, we can remove the periodic, repetitive patterns of any statistical textures using the inverse Fourier transform. In the restored image, the homogeneous region in the original image will have an approximately uniform gray level, and yet the defective region will be distinctly preserved. This converts the difficult defect detection in textured images into a simple thresholding problem in nontextured images. The experimental results from a variety of real statistical textures have shown the efficacy of the proposed method.