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<p>The authors present a polarization reflectance model that uses the Fresnel reflection coefficients. This reflectance model accurately predicts the magnitudes of polarization components of reflected light, and all the polarization-based methods presented follow from this model. The authors demonstrate the capability of polarization-based methods to segment material surfaces according to varying levels of relative electrical conductivity, in particular distinguishing dielectrics, which are nonconducting, and metals, which are highly conductive. Polarization-based methods can provide cues for distinguishing different intensity-edge types arising from intrinsic light-dark or color variations, intensity edges caused by specularities, and intensity edges caused by occluding contours where the viewing direction becomes nearly orthogonal to surface normals. Analysis of reflected polarization components is also shown to enable the separation of diffuse and specular components of reflection, unobscuring intrinsic surface detail saturated by specular glare. Polarization-based methods used for constraining surface normals are discussed.</p>
intrinsic light-dark variations; pattern recognition; surface segmentation; machine vision; polarization reflectance model; Fresnel reflection coefficients; electrical conductivity; color variations; intensity edges; light polarisation; light reflection; optical information processing; pattern recognition; reflectivity

L. Wolff and T. Boult, "Constraining Object Features Using a Polarization Reflectance Model," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 13, no. , pp. 635-657, 1991.
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