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Proceedings Paper

Multi-scale bi-domain Bayesian classifier designed for infrared image segmentation
Author(s): Qianjin Zhang; Lei Guo
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Paper Abstract

An extended Bayesian classifier, which is able to fuse information in original image and in its wavelet domain, is designed for infrared image segmentation. The algorithm begins with a re-sampling process over the original image and a wavelet transformation of the original image. Then, the Spatially Variant Mixture Model (SVMM) is applied in the bootstrap samples and the wavelet coefficients. The corresponding parameters are estimated by EM (Expectation Maximum) algorithm. Finally, a two-element Bayesian classifier is constructed. One part of the classifier is designed to exploit information in the original image, and the other part is designed to exploit information obtained in the wavelet domain. Theoretic analysis and experimental results confirms that the approach is efficient for infrared image segmentation, robust to noise and less computationally involved.

Paper Details

Date Published: 15 November 2007
PDF: 6 pages
Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 678812 (15 November 2007); doi: 10.1117/12.748917
Show Author Affiliations
Qianjin Zhang, Northwestern Polytechnical Univ. (China)
Lei Guo, Northwestern Polytechnical Univ. (China)

Published in SPIE Proceedings Vol. 6788:
MIPPR 2007: Pattern Recognition and Computer Vision
S. J. Maybank; Mingyue Ding; F. Wahl; Yaoting Zhu, Editor(s)

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