Real Time Textured-Image Segmentation Based On Noncausal Markovian Random Field Models

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Abstract
Two conceptually new algorithms are presented for segmenting textured images into regions in each of which the data is modelled as one of C non-causal 2-D Markovian Stochastic Processes. The algorithms are designed to operate in real time when implemented on new parallel computer architectures. A doubly stochastic representation is used in image modelling. Here, an auto-normal (Gaussian) process is used to model textures in visible light and infrared images, and an auto-binary field is used to model apriori information about local image geometry. Image segmentation is realized as true maximum likelihood estimation. The first segmentation algorithm is hierarchical and uses a pyramid-like structure in new ways that exploit the mutual dependencies among disjoint pieces of a textured region. The second segmentation algorithm is a relaxation-type algorithm that arises naturally within the context of these non-causal Markovian Processes. It is a simple, true maximum likelihood estimator. The algorithms can be used separately or together. These issues and subtleties concerning the use of the Markovian processes are discussed.

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