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http://hdl.handle.net/2080/354
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| Title: | Classification of objects and background using parallel genetic algorithm based clustering |
| Authors: | Kanungo, P
Nanda, P K
Ghosh, A
Samal, U C |
| Keywords: | Segmentation
Genetic Algorithm
Genetic Algorithm
Clustering
Thersholding |
| Issue Date: | 2006 |
| Publisher: | BCET, Hubli, India |
| Citation: | Proceedings of the IEEE-International Conference on Signal and Image Processing, Dec 7-9, 2006, BCET, Hubli, India |
| Abstract: | In this paper, a novel strategy based on the notion of
threshold is proposed to accomplish segmentation of
objects and background in a scene. Optimal threshold
for two class and three classes problems are determined
from the histogram of featured pixel values as opposed
to the original normalized histogram. Genetic algorithm
(GA) and Parallel Genetic Algorithm (PGA) based
clustering algorithms are proposed to determine the
optimal thresholds for two as well as three class
problems. The optimal threshold could segment the
noisy images. Our results, for two class problems, could
be comparable with that of Otsu’s approach. Our
approach yielded satisfactory results even for
histograms having overlapping class distributions. |
| Description: | Copyright for this article belongs to the publisher of the proceedings |
| URI: | http://hdl.handle.net/2080/354 |
| Appears in Collections: | Conference Papers
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| kanungo1.pdf | | 811Kb | Adobe PDF | View/Open |
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