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Proceedings Paper

Pattern recognition for passive polarimetric data using nonparametric classifiers
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Paper Abstract

Passive polarization based imaging is a useful tool in computer vision and pattern recognition. A passive polarization imaging system forms a polarimetric image from the reflection of ambient light that contains useful information for computer vision tasks such as object detection (classification) and recognition. Applications of polarization based pattern recognition include material classification and automatic shape recognition. In this paper, we present two target detection algorithms for images captured by a passive polarimetric imaging system. The proposed detection algorithms are based on Bayesian decision theory. In these approaches, an object can belong to one of any given number classes and classification involves making decisions that minimize the average probability of making incorrect decisions. This minimum is achieved by assigning an object to the class that maximizes the a posteriori probability. Computing a posteriori probabilities requires estimates of class conditional probability density functions (likelihoods) and prior probabilities. A Probabilistic neural network (PNN), which is a nonparametric method that can compute Bayes optimal boundaries, and a -nearest neighbor (KNN) classifier, is used for density estimation and classification. The proposed algorithms are applied to polarimetric image data gathered in the laboratory with a liquid crystal-based system. The experimental results validate the effectiveness of the above algorithms for target detection from polarimetric data.

Paper Details

Date Published: 18 August 2005
PDF: 8 pages
Proc. SPIE 5888, Polarization Science and Remote Sensing II, 588816 (18 August 2005); doi: 10.1117/12.618980
Show Author Affiliations
Vimal Thilak, New Mexico State Univ. (United States)
Jatinder Saini, New Mexico State Univ. (United States)
David G. Voelz, New Mexico State Univ. (United States)
Charles D. Creusere, New Mexico State Univ. (United States)


Published in SPIE Proceedings Vol. 5888:
Polarization Science and Remote Sensing II
Joseph A. Shaw; J. Scott Tyo, Editor(s)

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