The use of maximum curvature points for the recognition of partially occluded objects

摘要

A graph-theoretic optimization method is used to recognize partially occluded objects from a 2-D image through the use of maximal cliques and a weight matching algorithm. The vertices of an occluded object image with high curvature values are classified by the objects which are hypothesized to be involved in the occlusion. A heuristic method is also developed to further improve the computational speed. A few typical examples are given to illustrate the accuracy of the optimization model as well as the simplicity of the companion heuristic method.