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Proceedings Paper

Performance modeling of vote-based object recognition
Author(s): Edwin S. Hong; Bir Bhanu; Grinnell Jones; Xiaobing Qian
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Paper Abstract

The focus of this paper is predicting the bounds on performance of a vote-based object recognition system, when the test data features are distorted by uncertainty in both feature locations and magnitudes, by occlusion and by clutter. An improved method is presented to calculate lower and upper bound predictions of the probability that objects with various levels of distorted features will be recognized correctly. The prediction method takes model similarity into account, so that when models of objects are more similar to each other, then the probability of correct recognition is lower. The effectiveness of the prediction method is validated in a synthetic aperture radar (SAR) automatic target recognition (ATR) application using MSTAR public SAR data, which are obtained under different depression angles, object configurations and object articulations. Experiments show the performance improvement that can obtained by considering the feature magnitudes, compared to a previous performance prediction method that only considered the locations of features. In addition, the predicted performance is compared with actual performance of a vote-based SAR recognition system using the same SAR scatterer location and magnitude features.

Paper Details

Date Published: 20 August 2003
PDF: 10 pages
Proc. SPIE 5077, Passive Millimeter-Wave Imaging Technology VI and Radar Sensor Technology VII, (20 August 2003); doi: 10.1117/12.486058
Show Author Affiliations
Edwin S. Hong, Univ. of Washington (United States)
Bir Bhanu, Univ. of California, Riverside (United States)
Grinnell Jones, Univ. of California, Riverside (United States)
Xiaobing Qian, Univ. of California, Riverside (United States)


Published in SPIE Proceedings Vol. 5077:
Passive Millimeter-Wave Imaging Technology VI and Radar Sensor Technology VII
Roger Appleby; Robert Trebits; David A. Wikner; James L. Kurtz, Editor(s)

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