Object detection and recognition via stochastic model-based image segmentation

Summary form only given. A stochastic model-based image segmentation technique that utilizes the tone descriptor for object detection and recognition has been developed. The image regions are characterized by region-dependent constant mean (average-gray level) and variance (variation of gray level), and the distribution of the regions is modeled by a stochastic model. For a nondiffracting computed tomography (CT) image it has been proved that (1) at the pixel level, the pixel images are the asymptotic normal random variables, (2) at the class level, the regions are a normal random field, and (3) at the picture level, the observed image is a finite normal mixture.<
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