Model-based recognition of articulated objects

Authors:
J. S. Ahn;B. Bhanu
Affiliations:
Center for Research in Intelligent Systems, University of California, B232 Bourns Hall, Riverside, CA;Center for Research in Intelligent Systems, University of California, B232 Bourns Hall, Riverside, CA
Venue:
Pattern Recognition Letters
Year:
2002

Citing 2
Cited 0

On the Verification of Hypothesized Matches in Model-Based Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of Articulated and Occluded Objects

IEEE Transactions on Pattern Analysis and Machine Intelligence

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Abstract

This paper presents a model-based matching technique for recognition of articulated objects (with two parts) and the poses of these parts in synthetic aperture radar (SAR) images. Using articulation invariants as features, the recognition system first hypothesizes the pose of the larger part and then the pose of the smaller part. Geometric reasoning is carried out to correct identification errors. The thresholds for the quality of match are determined dynamically by minimizing the probability of a random match for the recognition system. Results are presented using both occluded synthetic articulated object SAR signatures and actual signatures of articulated objects from the real-world data. The system performance is evaluated with respect to identification performance and accuracy of estimates for the poses of the object parts.