Directional textures auto-inspection using principal component analysis

Abstract

This paper describes a global image restoration scheme using a principal component analysis that can be used to inspect defects in directional textured surfaces automatically. Decomposing the gray level of image pixels into an ensemble of row vectors, the input spatial domain image is transformed into principal component space so that the directional textures are well approximated by first k major components and their corresponding weight vectors, named truncated component solution (TCS). Then the local defects will be revealed by applying image subtraction between the original image and the TCS. This procedure blurs all directional textures and preserves only the local defects that were initially embedded in the input image. These defects, if any, are finally extracted by thresholding. Experiments on a variety of product surfaces with directional textures such as straight, slanted, orthogonal, slanted orthogonal, and oblique linear primitives were conducted to demonstrate the effectiveness and robustness of the proposed method. Furthermore, some preliminary experiments were also conducted to demonstrate the proposed scheme was insensitive to horizontal and vertical shifting, changes in illumination, and image rotation.