Hierarchical Markov random fields with stable points

Gregory M. Budzban; Toney Stirewalt

doi:10.1117/12.179218

30 June 1994 Hierarchical Markov random fields with stable points

Gregory M. Budzban, Toney Stirewalt

Proceedings Volume 2304, Neural and Stochastic Methods in Image and Signal Processing III; (1994) https://doi.org/10.1117/12.179218
Event: SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, 1994, San Diego, CA, United States

Abstract

Markov random field techniques for region labeling have become prevalent in image processing research since the seminal work of Geman and Geman in the early 1 980's. Their use in actual working systems, however, has been hampered by a number ofdifficult problems. Perhaps the most intractable of the problems has been the convergence rate of the algorithm. In this paper, we present a technique that introduces stable points in the labeling array of the random field. The stable points are determined by using a simple statistical pixel classifier together with a confidencemeasure at each pixel. The most confident (top 1% )pixellabels are selected and these labels are used to initiate the evolution of the random field. The stable points introduce pockets of "certainty" in the evolution of the process. The labeling is locally stable and even small numbers of stable points vastly decrease convergence rates of the algorithm.

Citation Download Citation

Gregory M. Budzban and Toney Stirewalt "Hierarchical Markov random fields with stable points", Proc. SPIE 2304, Neural and Stochastic Methods in Image and Signal Processing III, (30 June 1994); https://doi.org/10.1117/12.179218

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