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Color science: concepts and methods, quantitative data and formulae. New York: Wiley. Zuiderveld, K. (1994). Graphics gems IV. In Contrast limited adaptive histogram equalization (pp. 474–485). New York: Academic Press. Chapter 4 Reflectance Modelling In computer vision, the modelling of surface reflectance is a topic of pivotal importance for purposes of shape analysis and pattern recognition. In relation to photometric invariants, a number of physically meaningful parameters of reflectance models are material intrinsic and therefore potentially useful for pattern recognition tasks.
H. (2001). Simulation of digital camera images from hyperspectral input. In C. ), Vision models and applications to image and video processing (pp. 123–150). Dordrecht: Kluwer. Peli, E. (1990). Contrast in complex images. Journal of the Optical Society of America A, 7(10), 2032–2040. , & Hancock, E. R. (2006). Testing new variants of the Beckmann–Kirchhoff model against radiance data. Computer Vision and Image Understanding, 102(2), 145–168. , & Westall, J. (2005). Re-coloring images for gamuts of lower dimension.
4. In the figure, we denote the angle between the mean surface normal N and the micro-facet normal N as ϑ . Furthermore, θip and θsp are the projections of θi and θs onto the plane spanned by the facet normal N and the mean surface normal N . In other words, θip and θsp are, respectively, the angles between N and the projections of L and V onto the above plane. As before, the Fresnel reflection coefficient F (θi , η(u, λ)) is employed to model the penetration of the incident light into the material body.