Image segmentation with genetic algorithms: a formulation and implementation

Gunasekaran Seetharaman; Amruthur Narasimhan; Anand Sathe; Lisa Storc

doi:10.1117/12.48385

1 October 1991 Image segmentation with genetic algorithms: a formulation and implementation

Gunasekaran Seetharaman, Amruthur Narasimhan, Anand Sathe, Lisa Storc

Proceedings Volume 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision; (1991) https://doi.org/10.1117/12.48385
Event: San Diego, '91, 1991, San Diego, CA, United States

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.

Citation Download Citation

Gunasekaran Seetharaman, Amruthur Narasimhan, Anand Sathe, and Lisa Storc "Image segmentation with genetic algorithms: a formulation and implementation", Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); https://doi.org/10.1117/12.48385

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