Image segmentation and labeling using the Polya urn model

We propose a segmentation method based on Polya's urn model for contagious phenomena. A preliminary segmentation yields the initial composition of an urn representing the pixel. The resulting urns are then subjected to a modi fied urn sampling scheme mimicking the development of an infection to yield a segmentation of the image into homogeneous regions. This process is impl emented using contagion urn processes and generalizes Polya's scheme by all owing spatial interactions, The composition of the urns is iteratively upda ted by assuming a spatial Markovian relationship between neighboring pixel labels. The asymptotic behavior of this process is examined and comparisons with simulated annealing and relaxation labeling are presented. Examples o f the application of this scheme to the segmentation of synthetic texture i mages, ultra-wideband synthetic aperture radar (UWB SAR) images and magneti c resonance images (MRI) are provided.