Effective image segmentation with flexible ICM-based Markov random fields in distributed systems of personal computers

This paper presents the implementation of modified Markov Random Fields (MR Fs) in distributed systems of personal computers. Gibbs Random Fields (GRFs ) operating in the iterated conditional mode (ICM), modified to incorporate the flexibility of selecting from a continuum of configurations ranging fr om greater fidelity to the original image to more contextual influence (and enhanced smoothing), are presented, implemented in a distributed system of personal computers, and assessed for image segmentation. The characteristi cs of the distributed system, the message interchange mechanisms, the strat egy for the implementation of the MRF, as well as the statistical character ization of the performance in terms of hardware utilization, bottlenecks an d speed-up are presented and discussed. The results indicate that, despite their relative computational complexity, the developed concurrent system pr esents good potential for allowing MRFs to be executed in real-time for man y applications in image processing and computer vision. (C) 2000 Academic P ress.