Recognizing And Locating Partially Occluded Objects: Symbolic Clustering Method
Vincent Shang-Shouq Hwang · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1988
Recognition of objects using model can be formulated as the finding of affine transforms such that the locations of all object features are consistent with the projected positions of the model from a single view. This paper describes an efficient method for the computing of the transform using the symbolic clustering method. Matches between image features and object features are explored to generate hypotheses of possible object locations. Consistent hypotheses are grouped to form clusters. Supporting evidence of the participating hypotheses of a cluster is collected to generate a new transform hypothesis. The clusters that contain sufficient amount of evidence are selected for further verification. Hypotheses are verified by comparing the object against the image directly. The advantage of this approach is that the basic hypotheses can be computed easily and in parallel and the clusters can be generated efficiently. Also, since clusters with strong supports are selected and investigated first, the probability that the correct transforms are computed earlier in the hypothesize-and-test process is high. Therefore, the total amount of computation for the recognition task may be reduced.