Occlusion, Clutter, and Illumination Invariant Object Recognition

An object recognition system for industrial inspection that recognizes objects under similarity transformations in real time is proposed. It uses novel similarity measures that are inherently robust against occlusion, clutter, and nonlinear illumination changes. They can be extended to be robust to global as well as local contrast reversals. The matching is performed based on the maxima of the similarity measure in the transformation space. For normal applications, subpixel-accurate poses are obtained by extrapolating the maxima of the similarity measure from discrete samples in the transformation space. For applications with very high accuracy requirements, least-squares adjustment is used to further refine the extracted pose.