An Automatic Parameter Adjustment Method of Pulse Coupled Neural Network for Image Segmentation

Authors:
Masato Yonekawa;Hiroaki Kurokawa
Affiliations:
School of Computer Science, Tokyo University of Technology, Tokyo, Japan;School of Computer Science, Tokyo University of Technology, Tokyo, Japan
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Year:
2009

Citing 8
Cited 3

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Perfect image segmentation using pulse coupled neural networks

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Foveation by a pulse-coupled neural network

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The parameter optimization of the pulse coupled neural network for the pattern recognition

ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
An effect of inhibitory connections on synchronous firing assembly in the inhibitory connected pulse coupled neural network

ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
The capacity and the versatility of the pulse coupled neural network in the image matching

ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I

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Abstract

A Pulse Coupled Neural Network (PCNN) is proposed as a numerical model of cat visual cortex, and it has been applied to the engineering fields especially in an image processing, e.g., segmentation, edge enhancement, and so on. The PCNN model consists of neurons with two kind of inputs, namely feeding input and linking input and they each have a lot of parameters. The Parameters are used to be defined empirically and the optimization of parameters has been known as one of the remaining problem of PCNN. According to the recent studies, parameters in PCNN will be able to be given using parameter learning rule or evolutionary programming. However these methods require teaching images for the learning. In this study, we propose a parameter adjustment method of PCNN for the image segmentation. The proposed method changes the parameters through the iterations of trial of segmentation and the method doesn't require any teaching signal or teaching pattern. The successful results are obtained in the simulations, and we conclude that the proposed method shows good performance for the parameter adjustment of PCNNs.