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- W2008441340 abstract "In this work, we combine Bayesian techniques with a color categorization model, which leads to a method for the linguistic segmentation of color images. The categorization model considers the 11 universal color categories proposed by Berlin and Kay [Basic Color Terms: Their Universality and Evolution. Berkeley: University of California; 1969]. The likelihood for each category is represented by a linear combination of quadratic splines, and as a result, each voxel in the color space L*u*v* is described as a vector of probabilities, whose components express the degree to which the voxel belongs to a given color category. This gives rise to a probabilistic dictionary which is used for the segmentation, in which prior spatial granularity constraints are incorporated via an entropy-controlled quadratic Markov measure field (ECQMMF) model, as proposed by Rivera et al. [IEEE Trans Image Process 2007;16:3047–3057]. We give a generalization of ECQMMF that allows one to consider the perceptual interactions between the basic colors that were experimentally established by Boynton and Olson [Color Res Appl 1987;12:94–105]. © 2009 Wiley Periodicals, Inc. Col Res Appl, 34, 299–309, 2009" @default.
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- W2008441340 date "2009-08-01" @default.
- W2008441340 modified "2023-09-24" @default.
- W2008441340 title "Linguistic color image segmentation using a hierarchical Bayesian approach" @default.
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- W2008441340 doi "https://doi.org/10.1002/col.20509" @default.
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