@string{springer="Springer"}
@INCOLLECTION{Qazi-etal2012,
  author = {Imtnan-Ul-Haque Qazi and Olivier Alata and Zoltan Kato},
  title = {Parametric Stochastic Modeling for Color Image Segmentation and Texture
	Characterization},
  booktitle = {Advanced color image processing and analysis},
  publisher = springer,
  year = {2012},
  editor = {Christine Fernandez-Maloigne},
  chapter = {9},
  pages = {279--326},
  month = jul,
  abstract = {Splitting an input image into connected sets of pixels is the purpose
	of image segmentation. The resulting sets, called regions, are defined
	based on visual properties extracted by local features. To reduce
	the gap between the computed segmentation and the one expected by
	the user, these properties tend to embed the perceived complexity
	of the regions and sometimes their spatial relationship as well.
	Therefore, we developed different segmentation approaches, sweeping
	from classical color texture to recent color fractal features, in
	order to express this visual complexity and show how it can be used
	to express homogeneity, distances, and similarity measures. We present
	several segmentation algorithms, like RJMCMC and CSC, and provide
	examples for different parameter settings of features and algorithms.
	The now classical segmentation approaches, like pyramidal segmentation
	and watershed, are also presented and discussed, as well as the graph-based
	approaches. For the active contour approach a diffusion model for
	color images is proposed. Before drawing the conclusions, we talk
	about segmentation performance evaluation, including the concepts
	of closed-loop segmentation, supervised segmentation and quality
	metrics, i.e., the criteria for assessing the quality of an image-segmentation
	approach. An extensive list of references that covers most of the
	relevant related literature is provided.}
}