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Proceedings Paper

Recognizing And Locating Partially Occluded Objects: Symbolic Clustering Method
Author(s): Vincent S. S. Hwang
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Paper Abstract

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.

Paper Details

Date Published: 29 March 1988
PDF: 8 pages
Proc. SPIE 0937, Applications of Artificial Intelligence VI, (29 March 1988); doi: 10.1117/12.946954
Show Author Affiliations
Vincent S. S. Hwang, University of Texas at Austin (United States)

Published in SPIE Proceedings Vol. 0937:
Applications of Artificial Intelligence VI
Mohan M. Trivedi, Editor(s)

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