Polarization-based optical characterization for color texture analysis and segmentation

• Hand-crafted texture characterization based on light polarization signatures. • Image segmentation relying on pixel based circular polarization signature. • Multispectral image (185 bands) with five total variation planes. • The Karhunen-Loeve transform and then k-means were applied to the 185 bands. • Superior segmentation accuracy (compared with classic k-means on RGB). Texture characterization is very useful for automatic analysis of object surface images for a plethora of applications in medicine, agriculture, industry or remote sensing. Various texture characterization techniques exist, from the classical Haralick descriptors, Gabor filters, local binary patterns to automatically-extracted features using machine learning models. We propose a new hand-crafted texture characterization technique, based on light polarization property, by deploying a circular polarization filter (rotated from 0° to 360° in steps of 10°) in the image acquisition process. The hypothesis is that different materials and surfaces will exhibit different polarization signatures defined as pixel values variation as a function of polarization angle. Such polarization signature is able to locally characterize texture as a consequence of light reflections captured in every pixel due to the texture intrinsic variations. We show the usefulness of our approach for surface/material classification for the purpose of color image segmentation of natural outdoor scenes.