Extracting features to recognize partially occluded objects
Mark W. Koch, Arjun Ramamurthy · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1991
Noisy objects, partially occluded objects, and objects in random positions and orientations cause significant problems for current robotic vision systems. In the past, an association graph has formed the basis for many model based matching methods. However, the association graph has many false nodes due to local and noisy features. Objects having similar local structures create many false arcs in the association graph. The maximal clique recursive and tree search procedures for finding sets of structurally compatible matches have exponential time complexity, due to these false arcs and nodes. This represents a real problem as the number of objects appearing in the scene and the model set size increase. Our approach is similar to randomized string matching algorithms. Points on edges represent the model features. A fingerprint defines a subset of model features that uniquely identify the model. These fingerprints remove the ambiguities and inconsistencies present in the association graph and eliminate the problems of Turney's connected salient features. The vision system chooses the fingerprints at random, preventing a knowledgeable adversary from constructing examples that destroy the advantages of fingerprinting. Fingerprints consist of local model features called point vectors. We have developed a heuristic approach for extracting fingerprints from a set of model objects. A list of connected and unconnected scene edges represent the scene. A Hough transform type approach matches model fingerprints to scene features. Results are given for scenes containing varying amounts of occlusion.