Simple Parallel Hierarchical and Relaxation Algorithms for Segmenting Noncausal Markovian Random Fields.
BROWN UNIV PROVIDENCE RI LAB FOR ENGINEERING MAN/MACHINE SYSTEMS
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The modelling and segmentation of images by MRFs Markov Random Fields is treated. Two conceptually new algorithms are presented for segmenting textured images into regions in each of which the data is modelled as one of the C MRFs. The algorithms are designed to operate in real time when implemented on new parallel computer architectures that can be used in image modelling. A Gaussian MRF is used to model textures in visible light and infrared images, and an auto-binary MRF to model a priori information about local image geometry. Image segmentation is realized as maximum likelihood estimation. In addition to providing a mathematically correct means for introducing geometric structure, the auto-binary MRF can be used in a generative mode to generate image geometries and artificial images, and such stimulations constitute a very powerful tool for studying the effects of these models and the appropriate choice of model parameters. 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 arise naturally within the context of these noncausal MRFs. It is a simple, maximum likelihood estimator. The algorithms can be used separately or together. Author
- Statistics and Probability