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

Fernand S Cohen; David B Cooper

doi:10.1117/12.939219

16 February 1984 Real Time Textured-Image Segmentation Based On Noncausal Markovian Random Field Models

Fernand S Cohen, David B Cooper

Proceedings Volume 0449, Intelligent Robots: 3rd Intl Conf on Robot Vision and Sensory Controls; (1984) https://doi.org/10.1117/12.939219
Event: 1983 Cambridge Symposium, 1983, Cambridge, United States

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.

Citation Download Citation

Fernand S Cohen and David B Cooper "Real Time Textured-Image Segmentation Based On Noncausal Markovian Random Field Models", Proc. SPIE 0449, Intelligent Robots: 3rd Intl Conf on Robot Vision and Sensory Controls, (16 February 1984); https://doi.org/10.1117/12.939219

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