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Proceedings Paper

Image segmentation with genetic algorithms: a formulation and implementation
Author(s): Gunasekaran Seetharaman; Amruthur Narasimhan; Anand Sathe; Lisa Storc
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Paper Abstract

Image segmentation is an important step in any computer vision system. Segmentation refers to the partitioning of the image plane into several regions, such that each region corresponds to a logical entity present in the scene. The problem is inherently NP, and the theory on the existence and uniqueness of the ideal segmentation is not yet established. Several methods have been proposed in literature for image segmentation. With the exception of the state-space approach to segmentation, other methods lack generality. The state-space approach, however, amounts to searching for the solution in a large search space of 22n(2) possibilities for a n X n image. In this paper, a classic approach based on state-space techniques for segmentation due to Brice and Fennema is reformulated using genetic algorithms. The state space representation of a partially segmented image lends itself to binary strings, in which the dominant substrings are easily explained in terms of chromosomes. Also the operations such as crossover and mutations are easily abstracted. In particular, when multiple images are segmented from an image sequence, fusion of constraints from one to the other becomes clear under this formulation.

Paper Details

Date Published: 1 October 1991
PDF: 5 pages
Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); doi: 10.1117/12.48385
Show Author Affiliations
Gunasekaran Seetharaman, Univ. of Southwestern Louisiana (United States)
Amruthur Narasimhan, Univ. of Southwestern Louisiana (United States)
Anand Sathe, Univ. of Southwestern Louisiana (United States)
Lisa Storc, Cogent Research Inc. (United States)

Published in SPIE Proceedings Vol. 1569:
Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision
Su-Shing Chen, Editor(s)

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