Polarization phase-based method for material classification and object recognition in computer vision

A robust and accurate polarization phase-based technique for material classification is presented. The novelty of this technique is three-fold In (i) its theoretical development, (ii) its application, and, (iii) its experimental implementation. The concept of phase of polarization of a light wave is introduced to computer vision for discrimination between materials according to their intrinsic electrical conductivity, such as distinguishing conducting metals, and poorly-conducting dielectrics. Previous work has used intensity, color and polarization component ratios. This new method is based on the physical principle that metals retard orthogonal components of light upon reflection while dielectrics do not. This method has significant complementary advantages with respect to existing techniques, is computationally efficient, and can be easily implemented with existing imaging technology. Experiments for real circuit board inspection, non-conductive and conductive glass, and, outdoor object recognition have been performed to demonstrate its accuracy and potential capabilities.