Binary classification of Mueller matrix images from an optimization of Poincaré coordinates

Meredith K. Kupinski, Jaden Bankhead, Adriana Stohn, Russell Chipman

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)983-990
Number of pages8
JournalJournal of the Optical Society of America A: Optics and Image Science, and Vision
Issue number6
StatePublished - Jun 1 2017

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Computer Vision and Pattern Recognition


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