PCNN document segmentation method based on bacterial foraging optimization algorithm

Yanping Liao; Peng Zhang; Qiang Guo; Jian Wan

doi:10.1117/12.2064513

16 April 2014 PCNN document segmentation method based on bacterial foraging optimization algorithm

Yanping Liao, Peng Zhang, Qiang Guo, Jian Wan

Proceedings Volume 9159, Sixth International Conference on Digital Image Processing (ICDIP 2014); 91591T (2014) https://doi.org/10.1117/12.2064513
Event: Sixth International Conference on Digital Image Processing, 2014, Athens, Greece

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.

Citation Download Citation

Yanping Liao, Peng Zhang, Qiang Guo, and Jian Wan "PCNN document segmentation method based on bacterial foraging optimization algorithm", Proc. SPIE 9159, Sixth International Conference on Digital Image Processing (ICDIP 2014), 91591T (16 April 2014); https://doi.org/10.1117/12.2064513

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available