Pugh, Edward N
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Publication Adaptive Algorithms for 2–Channel Polarization Sensing under Various Polarization Statistics with Non-Uniform Distributions(2006-08-01) Yemelyanov, Konstantin M; Pugh, Edward N; Lin, Shih-Schön; Engheta, NaderThe polarization of light carries much useful information about the environment. Biological studies have shown that some animal species use polarization information for navigation and other purposes. It has been previously shown that a bio-inspired Polarization Difference Imaging technique can facilitate detection and feature extraction of targets in scattering media. It has also been established by S. Tyo1 that "Polarization Sum" and "Polarization Difference" are the optimum pair of linear combinations of images taken through two orthogonally oriented linear polarizers of a scene having a uniform distribution of polarization directions. However, in many real environments the scene has a non-uniform distribution of polarization directions. Using principal component analysis of the polarization statistics of the scene, here we develop a method to determine the two optimum information channels with unequal weighting coefficients that can be formed as linear combinations of the images of a scene taken through a pair of linear polarizers not constrained to the horizontal and vertical directions of the scene We determine the optimal orientations of linear polarization filters that enhance separation of a target from the background, where the target is defined as an area with distinct polarization characteristics as compared to the background. Experimental results confirm that in most situations adaptive polarization difference imaging outperforms "conventional" polarization difference imaging with fixed channels.Publication Separation and contrast enhancement of overlapping cast shadow components using polarization(2006-08-01) Lin, Shih-Schön; Pugh, Edward N; Yemelyanov, Konstantin M; Engheta, NaderShadow is an inseparable aspect of all natural scenes. When there are multiple light sources or multiple reflections several different shadows may overlap at the same location and create complicated patterns. Shadows are a potentially good source of information about a scene if the shadow regions can be properly identified and segmented. However, shadow region identification and segmentation is a difficult task and improperly identified shadows often interfere with machine vision tasks like object recognition and tracking. We propose here a new shadow separation and contrast enhancement method based on the polarization of light. Polarization information of the scene captured by our polarization-sensitive camera is shown to separate shadows from different light sources effectively. Such shadow separation is almost impossible to realize with conventional, polarization-insensitive imaging.