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• W2912998632 abstract "This thesis introduces a maximum entropy approach to model surface reflectance spectra. A reflectance spectrum is the amount of light, relative to the incident light, reflected from a surface at each wavelength. While the color of a surface can be in 3D vector form such as RGB, CMY, or YIQ, this thesis takes the surface reflectance spectrum to be the color of a surface. A reflectance spectrum is a physical property of a surface and does not vary with the different interactions a surface may undergo with its environment. Therefore, models of reflectance spectra can be used to fuse camera sensor responses from different images of the same surface or multiple surfaces of the same scene. This fusion improves the spectral estimates that can be obtained, and thus leads to better estimates of surface colors. The motivation for using a maximum entropy approach stems from the fact that surfaces observed in our everyday life surroundings typically have broad and therefore high entropy spectra. The maximum entropy approach, in addition, imposes the fewest constraints as it estimates surface reflectance spectra given only camera sensor responses. This is a major advantage over the widely used linear basis function spectral representations, which require a prespecified set of basis functions. Experimental results show that surface spectra of Munsell and construction paper patches can be successfully estimated using the maximum entropy approach in the case of three different surface interactions with the environment. First, in the case of changes in illumination, the thesis shows that the spectral models estimated are comparable to those obtained from the best approach which computes spectral models in the literature. Second, in the case of changes in the positions of surfaces with respect to each other, interreflections between the surfaces arise. Results show that the fusion of sensor responses from interreflection and no-interreflection regions allows for the surface spectrum computation, which would not be possible otherwise in the case of one surface patch scenes. Finally, in the case of interaction with a medium such as water, results show that surface spectral estimates improve upon combining multiple images of the same surface patch at different depths. Results also show that the larger the number of surface patches considered, the better the spectral estimates obtained." @default.
• W2912998632 created "2019-02-21" @default.
• W2912998632 creator A5046392298 @default.
• W2912998632 date "2009-01-01" @default.
• W2912998632 modified "2023-09-27" @default.
• W2912998632 title "Spectral models for color vision" @default.
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