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Detection of specularity using stereo in color and polarization space

We propose a specularity detection method based on a synergistic integration of color and polarization information from multiple views. The presence of specularity in images can cause many traditional computer vision processes to produce misleading results. Recent physics-based direction methods have provided much success in locating specularity; however no single cue reveals the full structure of specular reflections. Our method merges multiple-view color and polarization, two physically independent but highly effective cues, in a way that they mutually benefit from each other's strengths. In a fused color and polarization space, the proposed algorithm compares the distributions of sensor data from multiple views for detection of specular. We show that the efficacy of this new cue extends beyond that of the sum of its parts, and confirming experimental results are presented.