Paper Info
Title
A parallel image segmentation algorithm using relaxation with varying neighborhoods and its mapping to array processors
Abstract
This paper presents a segmentation algorithm based on deterministic relaxation with varying neighborhood structures for the segmentation of noisy images. The image is modeled as a discrete-valued Markov random field (MRF), or equivalently a Gibbs random field, corrupted by additive, independent, Gaussian noise; although, additivity and Gaussian assumptions are not necessary for the algorithm. The algorithm seeks to determine the maximum a posteriori (MAP) estimate of the noiseless scene. Using varying neighborhoods during relaxation helps pick up certain directional features in the image which are otherwise smoothed out. The parallelism of the algorithm is underscored by providing its mapping to mesh-connected and systolic array processors suitable for VLSI implementation. Segmentation results are given for 2- and 4-level Gibbs distributed and geometric images corrupted by noise of different levels. A comparative study of this segmentation algorithm with other relaxation algorithms and a single-sweep dynamic programming algorithm, all seeking the MAP estimate, is also presented.
Year
DOI
Venue
1987
10.1016/S0734-189X(87)80136-6
Computer Vision, Graphics, and Image Processing
Field
DocType
Volume
Mathematical optimization,Scale-space segmentation,Segmentation,Markov random field,Segmentation-based object categorization,Image segmentation,Gaussian,Maximum a posteriori estimation,Gaussian noise,Mathematics
Journal
38
Issue
ISSN
Citations 
2
0734-189X
19
PageRank 
References 
Authors
30.12
5
2
Name
Order
Citations
PageRank
Haluk Derin1162151.29
Chee Sun Won257387.74