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Proceedings Paper

Confidence analysis of target recognition
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Paper Abstract

In the model-based recognition methods, the result's confidence is decided by the feature distance between the segmented region and the target model, and can be defined as the posterior probability that can be computed from the object and background's prior probability and conditional probability with Bayesian formula. However, when recognizing the target, many physical constrains, or image measurements of object region and background region, can be applied on the validation of the recognition result, and should be introduced into the confidence analysis. In this paper, we proposed a new method to analyze the target recognition quality by combining the physical constrains or prior knowledge into confidence analysis within the frame of mathematical statistic theory and Dempster-Shafer's evidence theory. In this method, the usability of the information sources is appraised with Kolmogorov-Smirnov test method and the different computation models to compute the belief value to classifier's result corresponding to the different information source types were also proposed. The method was tested on the real sequences of images, and the result indicated that the proposed method for confidence analysis is feasible and effective.

Paper Details

Date Published: 24 September 2001
PDF: 6 pages
Proc. SPIE 4554, Object Detection, Classification, and Tracking Technologies, (24 September 2001); doi: 10.1117/12.441629
Show Author Affiliations
Zhengrong Zuo, Huazhong Univ. of Science and Technology (China)
Tianxu Zhang, State Key Lab. for Image Processing and Intelligent Control (China)

Published in SPIE Proceedings Vol. 4554:
Object Detection, Classification, and Tracking Technologies
Jun Shen; Sharatchandra Pankanti; Runsheng Wang, Editor(s)

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