Mr. Jatinder Saini Profile

Jatinder Saini

Student at nLIGHT Inc

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Proceedings Article | 18 August 2005 Paper

Pattern recognition for passive polarimetric data using nonparametric classifiers

Vimal Thilak, Jatinder Saini, David Voelz, Charles Creusere

Proceedings Volume 5888, 588816 (2005) https://doi.org/10.1117/12.618980

KEYWORDS: Polarization, Polarimetry, Probability theory, Pattern recognition, Imaging systems, Detection and tracking algorithms, Neural networks, Liquid crystals, Calibration, Machine vision

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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.

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