Open AccessBinary classification of Mueller matrix images from an optimization of Poincaré coordinates
Author(s) -
Meredith Kupinski,
Jaden Robert Bankhead,
Adriana Stohn,
Russell A. Chipman
Publication year - 2017
Publication title -
journal of the optical society of america. a, optics, image science, and vision./journal of the optical society of america. a, online
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.803
H-Index - 158
eISSN - 1520-8532
pISSN - 1084-7529
DOI - 10.1364/josaa.34.000983
Subject(s) - mueller calculus , polarimetry , binary number , polarization (electrochemistry) , pixel , computer science , pattern recognition (psychology) , divergence (linguistics) , artificial intelligence , optics , mathematics , physics , scattering , linguistics , chemistry , philosophy , arithmetic
A new binary classification method for Mueller matrix images is presented which optimizes the polarization state analyzer (PSA) and the polarization state generator (PSG) using a statistical divergence between pixel values in two regions of an image. This optimization generalizes to multiple PSA/PSG pairs so that the classification performance as a function of number of polarimetric measurements can be considered. Optimizing PSA/PSG pairs gives insight into which polarimetric measurements are most useful for the binary classification. For example, in scenes with strong diattenuation, retardance, or depolarization certain PSA/PSG pairs would make two regions in an image look very similar and other pairs would make the regions look very different. The method presented in this paper provides a quantitative method for ensuring the images acquired can be classified optimally.