02313nas a2200169 4500008004100000020002200041245009300063210006900156260007100225300001400296520160900310100002601919700001801945700001701963700003401980856012902014 2012 eng d a978-1-4419-6189-100aParametric Stochastic Modeling for Color Image Segmentation and Texture Characterization0 aParametric Stochastic Modeling for Color Image Segmentation and  aBerlin; Heidelberg; New York; London; Paris; TokyobSpringerc2012 a279 - 3253 a

Black should be made a color of light Clemence Boulouque

Parametric stochastic models offer the definition of color and/or texture features based on model parameters, which is of interest for color texture classification, segmentation and synthesis.

In this chapter, distribution of colors in the images through various parametric approximations including multivariate Gaussian distribution, multivariate Gaussian mixture models (MGMM) and Wishart distribution, is discussed. In the context of Bayesian color image segmentation, various aspects of sampling from the posterior distributions to estimate the color distribution from MGMM and the label field, using different move types are also discussed. These include reversible jump mechanism from MCMC methodology. Experimental results on color images are presented and discussed.

Then, we give some materials for the description of color spatial structure using Markov Random Fields (MRF), and more particularly multichannel GMRF, and multichannel linear prediction models. In this last approach, two dimensional complex multichannel versions of both causal and non-causal models are discussed to perform the simultaneous parametric power spectrum estimation of the luminance and the chrominance channels of the color image. Application of these models to the classification and segmentation of color texture images is also illustrated.

 

1 aQazi, Imtnan-Ul-Haque1 aAlata, Oliver1 aKato, Zoltan1 aFernandez-Maloigne, Christine uhttps://www.inf.szte.hu/publication/parametric-stochastic-modeling-for-color-image-segmentation-and-texture-characterization