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| Title: | Parallel Genetic Algorithm based Textured Image Segmentation using Markov Random Field Model |
| Authors: | Nanda, P K
Panda, Sucheta
Kanungo, P |
| Issue Date: | 2004 |
| Publisher: | National Institute of Technology, Rourkela, India |
| Citation: | Proceedings of the National Conference on Power Signal Processing and Control, Nov 16-17 2004, Rourkela, India |
| Abstract: | In this paper, we address the problem of texture
in image segmentation in an unsupervised frame
work. Markov Random Field model is employed
to model the textured images. The problem is
formulated as a pixel labeling problem. The la-
bels as well as the MRF model parameters are as-
sumed to be unknown. A coarse grained notion
based Parallel Genetic Algorithm (PGA) is pro-
posed to estimate the pixel label together with the
model parameters. With the evolution of the al-
gorithm, the model parameters, starting from an
arbitrry value, evolve to converge to the optimal
estimates. The algorithm starts with arbitrary
pixel labels and evolve to converge eventually to
stable labels. In the proposed PGA algorithm the
crossover and mutation probabilities are adaptive
with the progress in generation. The algorithm is
validated for synthetic as well as real images. |
| Description: | Copyright for this article belongs to the proceedings publishers |
| URI: | http://hdl.handle.net/2080/359 |
| Appears in Collections: | Conference Papers
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| pknanda_spanda_pkanungo_APSC04.pdf | | 160Kb | Adobe PDF | View/Open |
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