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Proceedings Paper

Recognition of partially occluded objects using macro features
Author(s): Peter Y. Hsu; Anthony P. Reeves
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Paper Abstract

Objects that are partially visible in an image may be recognized by detecting a number of salient local object features that conform to a set of relative location constraints. Such vision systems contain two main computational components; a feature extraction mechanism and a matching strategy that relates sets of features and their locations to object classes. The macro feature approach represents an object by a small number of complex features; the objectives are to provide very robust feature extraction and to simplify the feature matching stage by minimizing the number of detected features. Described here is a macro feature vision system that uses the Generalized Hough Transform on significant regions of the object surface for local feature detection. The results of using this system to detect objects in multi-object images with partial occlusion are presented.

Paper Details

Date Published: 20 April 1993
PDF: 12 pages
Proc. SPIE 1827, Model-Based Vision, (20 April 1993); doi: 10.1117/12.143056
Show Author Affiliations
Peter Y. Hsu, Cornell Univ. (United States)
Anthony P. Reeves, Cornell Univ. (United States)

Published in SPIE Proceedings Vol. 1827:
Model-Based Vision
Hatem N. Nasr; Rodney M. Larson, Editor(s)

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