Presentation 2010/6/11
An effects of inhibitory connections on synchronous firing assembly in the inhibitory connected pulse coupled neural network
Masahiro YOSHIHARA, Masato YONEKAWA, Hiroaki KUROKAWA,
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Abstract(in English) The Pulse Coupled Neural Network (PCNN) had been proposed as a model of visual cortex and a lot of applications to the image processing have been proposed recently. Authors also have been proposed Inhibitory Connected PCNN (IC-PCNN) which shows good performances for the color image processing. The IC-PCNN is an extended model of the PCNN model which consists of neurons, conventional PCNN connections and inhibitory connections. In our recent study, we have been shown that the IC-PCNN can obtain successful results for the color image segmentation. In this study, we focus the color image segmentation of the noise added image and we show that an optimum of inhibitory connections to avoid an effect of the noise in the image exists. In other words, we show that the optimization of the inhibitory connection is valid for the color image segmentation. Also, we show the effect of the inhibitory connections to the characteristics of synchronous firing assembly using numerical experiments.
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Keyword(in English) pulse coupled neural network / inhibitory connection / synchronous firing assembly / image segmentation
Paper # NC2010-13,NLP2010-13
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Committee NC
Conference Date 2010/6/11(1days)
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Language JPN
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Title (in English) An effects of inhibitory connections on synchronous firing assembly in the inhibitory connected pulse coupled neural network
Sub Title (in English)
Keyword(1) pulse coupled neural network
Keyword(2) inhibitory connection
Keyword(3) synchronous firing assembly
Keyword(4) image segmentation
1st Author's Name Masahiro YOSHIHARA
1st Author's Affiliation School of Computer Science, Tokyo University of Technology()
2nd Author's Name Masato YONEKAWA
2nd Author's Affiliation School of Computer Science, Tokyo University of Technology
3rd Author's Name Hiroaki KUROKAWA
3rd Author's Affiliation School of Computer Science, Tokyo University of Technology
Date 2010/6/11
Paper # NC2010-13,NLP2010-13
Volume (vol) vol.110
Number (no) 83
Page pp.pp.-
#Pages 6
Date of Issue