Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30172
Adaptive Pulse Coupled Neural Network Parameters for Image Segmentation

Authors: Thejaswi H. Raya, Vineetha Bettaiah, Heggere S. Ranganath

Abstract:

For over a decade, the Pulse Coupled Neural Network (PCNN) based algorithms have been successfully used in image interpretation applications including image segmentation. There are several versions of the PCNN based image segmentation methods, and the segmentation accuracy of all of them is very sensitive to the values of the network parameters. Most methods treat PCNN parameters like linking coefficient and primary firing threshold as global parameters, and determine them by trial-and-error. The automatic determination of appropriate values for linking coefficient, and primary firing threshold is a challenging problem and deserves further research. This paper presents a method for obtaining global as well as local values for the linking coefficient and the primary firing threshold for neurons directly from the image statistics. Extensive simulation results show that the proposed approach achieves excellent segmentation accuracy comparable to the best accuracy obtainable by trial-and-error for a variety of images.

Keywords: Automatic Selection of PCNN Parameters, Image Segmentation, Neural Networks, Pulse Coupled Neural Network

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1332752

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1848

References:


[1] R. Eckhorn, H.J. Reitboeck, M. Arndt, and P.W. Dicke, "Feature linking vis synchronization among distributed assemblies: Simulation results from cat visual cortex and from simulations," in Neural comput., Vol 2, pp.293-307, 1990.
[2] H. S. Ranganath, G. Kuntimad, and J. L. Johnson, "pulse coupled neural networks for image processing," IEEE Southeastcon, Raleigh, NC, Mar. 1995.
[3] G. Kuntimad, "Pulse coupled neural networks for image processing," Ph.D. dissertation, Department of Computer Science, The University of Alabama in Huntsville, 1995.
[4] G. Kuntimad and H.S. Ranganath, "Perfect segmentation using pulse coupled neural networks," IEEE Transactions on Neural networks, Vol. 10, No. 3, pp. 591-598, 1999.
[5] J. A. Karvonen, "Baltic sea ice SAR segmentation and classification using modified pulse coupled neural networks," IEEE Transactions on Geoscience and Remote Sensing, Vol 42, No. 7, pp. 1566-1574, 2004.
[6] R. D. StewartI. Fermin, and M. Opper, "Region growing with pulse coupled neural networks: an alternative to seeded region growing. IEEE Transactions on Neural Networks, Vol. 13, No. 6, pp.1557-1562, 2002.
[7] Y. Ma, R. Dai, and L. Li, "Image segmentation of embryonic plant cell using pulse coupled neural networks," Chinease Science Bulletin, Vol. 47, No. 2, pp. 167-172, 2002.
[8] Y. Ma, Q. Liu, and Z. Quian, "Automated image segmentation using improved PCN model based on cross-entropy," Journal of Image and Graphics, Vol. 10, pp. 579-584, 2005.
[9] Xiao, Shi, and Chang, "Automatic image segmentation based on PCNN and fuzzy mutual information," IEEE Ninth International Conference on Computer and Information Technology, pp. 241-245, 2009.