Toward a complete adaptive analysis of an image

We present an adaptive method that allows the classifi- cation of the regions of an image in two classes: textured and uni- form regions (weak gray-level variance). Information on the type of texture (stochastic, deterministic) and its granularity (macroscopic, microscopic) are extracted. The developed method is achieved in two steps. It first enables the determination of the global context of an image (image mainly composed of uniform areas or textured ones) and the localization of the textured and uniform areas. The second step characterizes each detected area by considering some appropriate features: mean and variance in the case of uniform re- gions and classical relevant texture attributes (derived from a statis- tical analysis) associated with some new attributes that we define in the case of textured regions. These complementary features are determined from a texture model derived from the Wold decompo- sition of the autocovariance function. They enable the acquisition of some information on the type of texture and its granularity. We show the efficiency of the method through two examples of image seg- mentation. © 2003 SPIE and IS&T. (DOI: 10.1117/1.1557152)

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