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

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.

[1]  R. Chipman,et al.  Interpretation of Mueller matrices based on polar decomposition , 1996 .

[2]  J Scott Tyo,et al.  Polarization components analysis for invariant discrimination. , 2007, Applied optics.

[3]  Christophe Collet,et al.  Clustering of polarization-encoded images. , 2004, Applied optics.

[4]  Frédéric Galland,et al.  Joint contrast optimization and object segmentation in active polarimetric images. , 2012, Optics letters.

[5]  François Goudail,et al.  Polarimetric target detection in the presence of spatially fluctuating Mueller matrices. , 2011, Optics letters.

[6]  F. Goudail,et al.  When is polarimetric imaging preferable to intensity imaging for target detection? , 2011, Journal of the Optical Society of America. A, Optics, image science, and vision.

[7]  François Goudail,et al.  Optimization of the contrast in active Stokes images. , 2009, Optics letters.

[8]  B. Jeune,et al.  Optical media and target characterization by Mueller matrix decomposition , 1996 .

[9]  Antonello De Martino,et al.  Adapted polarization state contrast image. , 2009, Optics express.

[10]  Tatiana Novikova,et al.  Ex vivo Mueller polarimetric imaging of the uterine cervix: a first statistical evaluation , 2016, Journal of biomedical optics.

[11]  François Goudail,et al.  Optimal discrimination of multiple regions with an active polarimetric imager. , 2011, Optics express.

[12]  François Goudail,et al.  Optimization of the contrast in polarimetric scalar images. , 2009, Optics letters.

[13]  François Goudail,et al.  Contrast optimization in broadband passive polarimetric imaging. , 2014, Optics letters.

[14]  François Goudail,et al.  Fully tunable active polarization imager for contrast enhancement and partial polarimetry. , 2012, Applied optics.

[15]  F. Goudail.  Noise minimization and equalization for Stokes polarimeters in the presence of signal-dependent Poisson shot noise. , 2009, Optics letters.

[16]  J. Scott Tyo,et al.  Classification using active polarimetry , 2012, Defense + Commercial Sensing.

[17]  J. Zallat,et al.  Optimal configurations for imaging polarimeters: impact of image noise and systematic errors , 2006 .

[18]  E Clarkson,et al.  Method for optimizing channelized quadratic observers for binary classification of large-dimensional image datasets. , 2015, Journal of the Optical Society of America. A, Optics, image science, and vision.