Polarization-based optical characterization for color texture analysis and segmentation

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 tech-niques 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 characteri-zation technique, based on light polarization property, by deploying a circular polarization filter (rotated from 0 degrees to 360 degrees in steps of 10 degrees) 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.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

Texture characterization is a valuable tool for analyzing object surface images in various fields. This paper proposes a new hand-crafted texture characterization technique based on light polarization property. The technique utilizes a circular polarization filter in the image acquisition process to capture polarization signatures that can locally characterize texture. Experimental results demonstrate the usefulness of the proposed method for surface/material classification and color image segmentation.