An artificial immune approach for optical image based vision inspection

This paper presents a novel approach of visual inspection for texture surface defects. The approach usesartificial immune theory in learning the detection of texture defects. In this paper, texture defects areregards as non-self, and normal textures are regarded as self. Defect filters and segmentation thresholdsused for defect detection are regarded as antibodies. The clonal selection algorithm stemmed from thenatural immune system is employed to learn antibodies. Experimental results on textile image inspectionare presented to illustrate the merit and feasibility of the proposed method.