Effective Image Segmentation with Flexible ICM-Based Markov Random Fields in Distributed Systems of Personal Computers
✍ Scribed by Odemir Martinez Bruno; Luciano da Fontoura Costa
- Publisher
- Elsevier Science
- Year
- 2000
- Tongue
- English
- Weight
- 540 KB
- Volume
- 6
- Category
- Article
- ISSN
- 1077-2014
No coin nor oath required. For personal study only.
✦ Synopsis
T his paper presents the implementation of modi®ed Markov Random Fields (MRFs) in distributed systems of personal computers. Gibbs Random Fields (GRFs) operating in the iterated conditional mode (ICM), modi®ed to incorporate the ¯exibility of selecting from a continuum of con®gurations ranging from greater ®delity to the original image to more contextual in¯uence (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.