Classification of Material Surfaces Using the Polarization of Specular Highlights

Wolff, Lawrence B.

Recently there has been interest, in computer vision research, in the segmentation of images based upon the actual material makeup of the objects or object parts that constitute image regions. The idea is to identify image characteristics which can be used to predict the material properties of objects that are being imaged. A majority of object surfaces can be simply classified according to their basic electrical properties; metal objects (e.g. Aluminum, Copper) conduct electricity rather well while dielectric objects (e.g. Rubber, Plastic, Ceramic) conduct electricity poorly. Distinguishing image regions according to whether they correspond to metal or dielectric material can provide important information for scene understanding especially in industrial machine vision. One such major application is circuit board inspection where the presence of dielectric or metal material in the wrong place can cause trouble. A previous approach to the problem of identifying metal or dielectric material in images is based upon careful spectral (i.e. color) analysis of reflected light from material objects. This paper presents a technique for identifying the material properties of objects in an image using a polarizing lens (i.e. Polaroid filter). Two images of the same scene are taken with a polarizing lens placed in front of a camera in two different respective orientations. Effectively these two images represent two linearly independent polarization components of the reflected light. It is shown that when the linearly independent components of polarization are taken parallel and perpendicular with respect to the plane in which specular rays travel that dielectric objects can be distinguished from metallic objects when specular highlights are present. In particular the two polarization components are very similar at specular highlights on metals while the two polarization components for specular highlights on dielectrics are very different, the perpendicular component having much larger magnitude than the parallel component. This is shown to hold regardless of whether the surface is polished or rough. Results for coated surfaces will be presented at a future date.



More About This Work

Academic Units
Computer Science
Department of Computer Science, Columbia University
Columbia University Computer Science Technical Reports, CUCS-333-88
Published Here
December 9, 2011