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Proceedings Paper

PCNN document segmentation method based on bacterial foraging optimization algorithm
Author(s): Yanping Liao; Peng Zhang; Qiang Guo; Jian Wan
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Paper Abstract

Pulse Coupled Neural Network(PCNN) is widely used in the field of image processing, but it is a difficult task to define the relative parameters properly in the research of the applications of PCNN. So far the determination of parameters of its model needs a lot of experiments. To deal with the above problem, a document segmentation based on the improved PCNN is proposed. It uses the maximum entropy function as the fitness function of bacterial foraging optimization algorithm, adopts bacterial foraging optimization algorithm to search the optimal parameters, and eliminates the trouble of manually set the experiment parameters. Experimental results show that the proposed algorithm can effectively complete document segmentation. And result of the segmentation is better than the contrast algorithms.

Paper Details

Date Published: 16 April 2014
PDF: 7 pages
Proc. SPIE 9159, Sixth International Conference on Digital Image Processing (ICDIP 2014), 91591T (16 April 2014); doi: 10.1117/12.2064513
Show Author Affiliations
Yanping Liao, Harbin Engineering Univ. (China)
Peng Zhang, Harbin Engineering Univ. (China)
Qiang Guo, Harbin Engineering Univ. (China)
Jian Wan, Harbin Engineering Univ. (China)


Published in SPIE Proceedings Vol. 9159:
Sixth International Conference on Digital Image Processing (ICDIP 2014)
Charles M. Falco; Chin-Chen Chang; Xudong Jiang, Editor(s)

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