Klinker, O. J. and Sharer, S. A. and Kansde, T. "Using a color reflection model to separate highlights from object color." Intrd. Conf. on Comp. Vsion, IEEE, 1987, 145-150.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....conditions (bright sunlight, day light, tube light) and tapers off near the specularfry boundary merging into the rest of the body color. Such specular regions and their adjacent colored regions when projected into a color space form characteristic clusters such as the skewed T described in [21]. These dusters can, therefore, be analysed to detect and remove highlights using the method described in that paper. Figures 4 6 demonstrate the color region segmentation algorithm. Figure 4a shows a 256 x 256 pixel size image of a color pattern on a plastic bag. The folding on the bag and its ....

G.J. Klinker, S.A. Sharer, T. Kanade, "Using a color reflection model to separate highlights from object color," Proc. Int. Conf. Computer Vision, June 1987.

....conditions (bright sunhght, day light, tube light) and tapers off near the specularity boundary merging into the rest of the body color. Such specular regions and their adjacent colored regions when projected into a color space form characteristic clusters such as the skewed T described in [21]. These dusters can, therefore, be analysed to detect and remove highlights using the method described in that paper. Figures 4 6 demonstrate the color region segmentation algorithm. Figure 4a shows a 256 x 256 pixel size image of a color pattern on a plastic bag. The folding on the bag and its ....

G.J. Klinker, S.A. Sharer, T. Kanade, "Using a color reflection model to separate highlights from object color," Proc. Int. Con Computer Vision, June 1987.

....was proposed using the dichromatic reflection model by Shafer [13] On a monochrome inhomogeneous dielectric object illuminated by a light source with a color distinct from the object, the color of specular highlights appear as a linear combination of these distinct colors vectors. Work by Klinker [12], 8] experimentally verified this approach. Gershon et al. 7] also used the dichromatic reflection model to identify specularities on inhomogeneous dielectrics. In [16] Vaillant examines using multiple viewpoints to detecting occluding contours. The elegance and advantage of using reflected ....

G. Klinker S. Shafer and T. Kanade. Using a color reflection model to separate highlights from object color. In Proc. IEEE ICCV, pages 145--150, London, 1987.

.... intrinsic reflectance is contaminated by an additive specular surface reflection complicating the extraction of object properties [Shaf85, Nov92b] By examining the apparent wavelength of the specular reflections in the image, it is possible to extract an estimate of the nature of the illuminant [DZmu86, LeeH86, Gers86, Klin87]. In the current context, performance on the extraction task is degraded if any of the color channels is saturated (see [Klin87] As the intensity is increased for a given camera configuration, saturation will result in all specularities, and thus all illuminants, appearing white. While the range ....

.... examining the apparent wavelength of the specular reflections in the image, it is possible to extract an estimate of the nature of the illuminant [DZmu86, LeeH86, Gers86, Klin87] In the current context, performance on the extraction task is degraded if any of the color channels is saturated (see [Klin87]) As the intensity is increased for a given camera configuration, saturation will result in all specularities, and thus all illuminants, appearing white. While the range of intensity spanned by an 8 bit representation may be increased in order to reduce the amount of specularity saturation, it is ....

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Klinker, G. F., Shafer, S. A. and Kanade, T. (1987) Using a color reflection model to separate highlights from object color. Proc. ICCV, pp. 145-150.

....large number of objects. In the mid 1980s, it was recognized that the color histogram for a single inhomogeneous surface with highlights will have a planar distribution in color space [7] It has since been shown that the colors do not fall randomly in a plane, but form clusters at specific points [8]. The Figure 1 shows a face image, the skin color occurrences in the RGB color space (256x256x256) and the skin color distribution in the normalized color space. It has been observed that (1) human skin colors cluster in a small region in a color space; 2) human skin colors differ more in ....

G.J. Klinker, S.A. Shafer, and T. Kanade. Using a color reflection model to separate highlights from object color. In Proc. ICCV, pages 145150, 1987.

.... number of objects [2] In the mid 1980s, it was recognized that the color histogram for a single inhomogeneous surface with highlights will have a planar distribution in color space [7] It has since been shown that the colors do not fall randomly in a plane, but form clusters at specific points [8]. It has been further observed that (1) human skin colors cluster in a small region in a color space; 2) human skin colors differ more in intensity than in colors, and (3) under a certain lighting condition, a skincolor distribution can be characterized by a multivariate normal distribution in ....

G.J. Klinker, S.A. Shafer, and T. Kanade, "Using a color reflection model to separate highlights from object color," Proc. ICCV, pp. 145-150, 1987.

.... number of objects [16] In the mid 1980s, it was recognized that the color histogram for a single inhomogeneous surface with highlights will have a planar distribution in color space [19] It has since been shown that the colors do not fall randomly in a plane, but form clusters at specific points [20, 21]. The color histograms of human skin coincide with these observations. The Figure 1 shows a face image and the skin color occurrences in the RGB color space (256x256x256) The skin colors are clustered in a small area in the RGB color space, i.e. only a few of all possible colors actually occur ....

G.J. Klinker, S.A. Shafer, and T. Kanade, "Using a color reflection model to separate highlights from object color," Proc. ICCV, pp. 145-150, 1987.

....procedure allows rapid creation of the model database. 1 Introduction In the paper we present a colour based recognition system that aims to demonstrate the advantages of selective processing. We do not attempt to analyse the whole image in the spirit of traditional segmentation methods (e.g. [KSK87], GJT87] instead we try to find areas where distinctive colour provides least ambiguous information about presence of objects from the model database. Using this approach, standard recognition tasks (e.g. What is in the scene , Where is object X ) can be accomplished without wasting computational ....

....and man made objects. In contrast, SPDs of a number of artificial illuminants are known. Furthermore, three basis functions providing practically a perfect fit to all phases of daylight have been found [WS82] The effects of geometry on SPD of reflected light have been extensively studied [HB87] [KSK87]. The dichromatic reflection model of [Sha84] is generally regarded to be accurate for a large class of materials [Tom91] The dichromatic model states that reflected light L consists of two independent components: light reflected on the interface and light due to sub surface (body) reflection. ....

G.J. Klinker, S.A. Shafer, and T. Kanade. Using a color reflection model to separate highlights from object color. In ICCV87 [ICC87], pages 145--150.

....how to determine the two characteristic vectors out of the infinite set of vec tors that could define the plane. However, in 1987 Klinker and Gershon independently observed that the color histogram does not uniformly fill a parallelogram, but instead forms a T shape or dog leg in color space [7] , 2] They showed that it is composed of two linear clusters, one corresponding to pixels that exhibit mostly body reflection and one corresponding to pixels that exhibit mostly surface reflection. This T shape made it possible to identify characteristic body reflection and illumina tion colors. ....

....than has been previously described. Color histograms have identifiable features that relate in a precise mathematical way to scene properties. Object color and illumination color are the most obvious properties that are related to color distribution, and their extraction has already been described [7], 5] 2] We show that the histogram of color variation may be further exploited to relate its shape to surface roughness and imaging geometry. Furthermore, an understanding of these fea tures allows us to make an improved estimate of illumination color and object color. 2. Color Histogram for a ....

Klinker, O. J. and Sharer, S. A. and Kansde, T. "Using a color reflection model to separate highlights from object color." Intrd. Conf. on Comp. Vsion, IEEE, 1987, 145-150.

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G. J. Klinker, S. A. Shafer, and T. Kanade, "Using color reflection model to separate highlights from object color," in Proceedings of the International Conference on Computer Vision (Institute of Electrical and Electronics Engineers, New York,

....1984] This plane is defined by two color vectors: a body reflection vector and a surface reflection vector. Every pixel s color is a linear combination of these two colors. In 1987 Klinker and Gershon independently observed that the color histogram forms a T shape or dog leg in color space [Klinker et al. 1987] , Gershon, 1987] They showed that it is composed of two linear clusters, one corresponding to pixels that exhibit mostly body reflection and one corresponding to pixels that exhibit mostly surface reflection. This T shape made it possible to identify characteristic body reflection and ....

G. J. Klinker, S. A. Shafer, and T. Kanade. Using a color reflection model to separate highlights from object color. In International Conference on Computer Vision (ICCV), pages 145--150, London, June 1987. IEEE.

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