Weighted Smallest Deformation Similarity for NN-Based Template Matching

Abstract

This article deals with the template matching problem, and a weighted smallest deformation similarity measure, which is robust to occlusions, background outliers, and complex deformations. The appearance-based nearest neighbor (NN) matching of points is constructed and the smallest location distance between each point in the template and its matching points is employed to penalize the deformation explicitly. Then, the weights are added to points in the template relied on their likelihood of belonging to the background through NN matching with the points around the target window. Experiments show that the proposed method improves the state-of-the-art performance on real-world scenario benchmarks and can be applied in rough positioning of surface mount technology components.