A computational model of vision attention for inspection of surface quality in production line

Abstract

There is need to detect regions of small defects in a large background, when product surface quality in line is inspected by machine vision systems. A computational model of visual attention was developed for solving the problem, inspired by the behavior and the neuronal architecture of human visual attention. Firstly, the global feature was extracted from input image by law’s rules, then local features were extracted and evaluated with an improved saliency map model of Itti. The local features were fused into a single topographical saliency map by a multi-feature fusion operator differenced from Itti model, in which the better feature has the higher weighting coefficient and more contribution to fusion of the feature’s images. Finally, the regions were “popped out” in the map. Experimental results show that the model can locate regions of interest and exclude the most background regions.