Color image segmentation using Markov random fields
- 7 January 2003
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 304-312
- https://doi.org/10.1109/cvpr.1989.37865
Abstract
The use of Markov random fields (MRFs) in color image segmentation of natural outdoor scenes is discussed. MRFs provide an elegant means of specifying a local energy function which embodies the expected dependencies of neighboring pixels and includes both the prior and posterior probabilistic distributions. This local neighborhood-based specification of dependencies avoids ad hoc brittle methods using global image knowledge. A brief analysis of ongoing research in color differencing methods is presented, since they are central to the problem of color segmentation. The authors develop and compare the use of three different lattice structures for coupled MRFs with line and color processes based on squares, hexagons, and triangles, and also discusses current efforts in MRF parameter understanding Author(s) Daily, M.J. Hughes Res. Lab., Malibu, CA, USAKeywords
This publication has 9 references indexed in Scilit:
- A color metric for computer visionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Parallel Integration of Vision ModulesScience, 1988
- The measurement of highlights in color imagesInternational Journal of Computer Vision, 1988
- Computational stereo vision using colorIEEE Control Systems Magazine, 1988
- “Neural” computation of decisions in optimization problemsBiological Cybernetics, 1985
- Statistical mechanics and disordered systemsCommunications of the ACM, 1985
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of ImagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1984
- Color information for region segmentationComputer Graphics and Image Processing, 1980
- Picture segmentation using a recursive region splitting methodComputer Graphics and Image Processing, 1978