Title

Markov Random Fields On A Simd Machine For Global Region Labeling

Abstract

The Markov Random Field (MRF) formulation allows independence over small pixel neighborhoods suitable for SIMD implementation. The equivalence between the Gibbs distribution over global configurations and MRF allows describing the problem as maximizing a probability, or equivalently, minimizing an energy function (EF). The EF is a convenient device for integrating "votes" from disparate, pre-processed features-mean intensity, variance, moments, etc. Contributions from each feature are simply weighted and summed. The EF is flexible and can be easily modified to capture a priori beliefs about the distribution of the configuration space, and still remain theoretically sound. A unique formulation of the EF is given. Notably, a deterministic edge finder contributes to the EF. Weights are independently assigned to each feature's report (indicators). Simulated annealing is the theoretical mechanism which guarantees convergence in distribution to a global minimum. Because the number of iterations is an exponential function of time, we depart from theory and implement a fast, heuristic "cooling" schedule. A videotape of results on simulated FUR imagery demonstrates real-time update over the entire image. Actual convergence is still too slow for real-time use (O( 1 mm.)), but the quality of results compares favorably with other region labelling schemes.

Publication Date

8-1-1991

Publication Title

Proceedings of SPIE - The International Society for Optical Engineering

Volume

1470

Number of Pages

175-182

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1117/12.44851

Socpus ID

33749904495 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/33749904495

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