Effective Image Segmentation with Flexible ICM-Based Markov Random Fields in Distributed Systems of Personal Computers

Abstract

This paper presents the implementation of modified Markov Random Fields (MRFs) 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 from 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 characteristics of the distributed system, the message interchange mechanisms, the strategy for the implementation of the MRF, as well as the statistical characterization of the performance in terms of hardware utilization, bottlenecks and speed-up are presented and discussed. The results indicate that, despite their relative computational complexity, the developed concurrent system presents good potential for allowing MRFs to be executed in real-time for many applications in image processing and computer vision.