Low Contrast Surface Inspection under Uneven Illumination Using Independent Subspace Analysis

Abstract

When using the machine vision technique to detect the low contrast surface image, the difference between the defect and the background of the low contrast surface is not obvious. The existing method is difficult to detect these defects, and the influence of uneven illumination makes the problem more difficult, the purpose of this paper is to detect defects in low-contrast surface images such as LCDs that are unevenly illuminated. The proposed method in this paper is based on the independent subspace analysis (ISA), which includes the learning and detection stage. In the training stage, a batch of defective low contrast images is used to find a set of independent basis images. During the test stage, the low contrast image to be measured is reconstructed by the nonlinear combination of a set of basis images obtained by learning, and the error between the reconstructed image and the sample to be measured is used to determine whether exist a defect. The experimental results show that the reconstruction image method based on the basis image through ISA has achieved good results.