Adaptive parameters determination method of pulse coupled neural network based on water valley area

Authors:
Min Li;Wei Cai;Zheng Tan
Affiliations:
School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi Province, P.R. China;Xi'an Research Inst. of Hi-Tech, Shaanxi Province, P.R.C.;School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi Province, P.R. China
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Year:
2006

Citing 4
Cited 0

Digital Mammogram Segmentation Algorithm Using Pulse Coupled Neural Networks

ICIG '04 Proceedings of the Third International Conference on Image and Graphics
Feature linking via synchronization among distributed assemblies: Simulations of results from cat visual cortex

Neural Computation
Perfect image segmentation using pulse coupled neural networks

IEEE Transactions on Neural Networks
Image shadow removal using pulse coupled neural network

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Pulse coupled neural network (PCNN) is different from traditional artificial neural networks, models of which have biological background and are based on the experimental observations of synchronous pulse bursts in the cat visual cortex. However, it is very difficult to determine the exact relationship between the parameters of PCNN model. Focusing on the famous difficult problem of PCNN, how to determine the optimum parameters automatically, this paper proposes the definition of water valley area, establishes a modified PCNN, and puts forward an adaptive PCNN parameters determination algorithm based on water valley area. Extensive experimental results on image processing demonstrate its validity and robustness.