TY - CHAP T1 - Parametric Stochastic Modeling for Color Image Segmentation and Texture Characterization T2 - Advanced color image processing and analysis Y1 - 2012 A1 - Imtnan-Ul-Haque Qazi A1 - Oliver Alata A1 - Zoltan Kato ED - Christine Fernandez-Maloigne AB -

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.

 

JF - Advanced color image processing and analysis PB - Springer CY - Berlin; Heidelberg; New York; London; Paris; Tokyo SN - 978-1-4419-6189-1 ER -