Modeling and segmentation of noisy and textured images using Gibbs random fields

Journal Article · Thu Jan 01 00:00:00 EST 1987 · IEEE Trans. Pattern Anal. Mach. Intell.; (United States)

DOI:https://doi.org/10.1109/TPAMI.1987.4767871· OSTI ID:6450171

Derin, H; Elliott, H

This paper presents a new approach to the use of Gibbs distributions (GD) for modeling and segmentation of noisy and textured images. Specifically, the paper presents random field models for noisy and textured image data based upon a hierarchy of GD. It then presents dynamic programming based segmentation algorithms for noisy and textured images, considering a statistical maximum a posteriori (MAP) criterion. Due to computational concerns, however, sub-optimal versions of the algorithms are devised through simplifying approximations in the model. Since model parameters are needed for the segmentation algorithms, a new parameter estimation technique is developed for estimating the parameters in a GD.

• Research Organization: Dept. of Electrical and Computer Engineering, Univ. of Massachusetts, Amherst, MA 01003
• OSTI ID: 6450171
• Journal Information: IEEE Trans. Pattern Anal. Mach. Intell.; (United States), Vol. PAMI-9:1
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
IMAGE PROCESSING
ALGORITHMS
MATHEMATICAL MODELS
ARTIFICIAL INTELLIGENCE
DATA COVARIANCES
DYNAMIC PROGRAMMING
IMAGES
STATISTICS
TEXTURE
MATHEMATICAL LOGIC
MATHEMATICS
PROCESSING
PROGRAMMING
990210* - Supercomputers- (1987-1989)