Presentation | 2013-10-28 Motion Picture Processing by Two-Layer Cellular Neural Networks with Switching Templates Kazushige NATSUNO, Yoko UWATE, Yoshifumi NISHIO, |
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Abstract(in English) | The structure of Cellular Neural Network(CNN) is grating coupling of analog circuits. The CNN can be used for various image processing applications by template.The template is the pattern of coupling that make a decision about behavior of CNN. In a general way, image processing of using conventional cellular neural networks is still image processing. On the other hand, it is few for motion picture processing. Therefore, we propose the new system which is effective structure for motion picture processing. In this paper, we investigate characteristics of output values by a proposed new system of two layer cellular neural networks (two-layer CNN). From simulation results, we confirm that the proposed system makes it possible to characterize the time course characteristic of moving object. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | image processing / motion picture / retina |
Paper # | NLP2013-71 |
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Committee | NLP |
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Conference Date | 2013/10/21(1days) |
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Registration To | Nonlinear Problems (NLP) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Motion Picture Processing by Two-Layer Cellular Neural Networks with Switching Templates |
Sub Title (in English) | |
Keyword(1) | image processing |
Keyword(2) | motion picture |
Keyword(3) | retina |
1st Author's Name | Kazushige NATSUNO |
1st Author's Affiliation | Department of Electrical and Electronic Engineering, Tokushima University() |
2nd Author's Name | Yoko UWATE |
2nd Author's Affiliation | Department of Electrical and Electronic Engineering, Tokushima University |
3rd Author's Name | Yoshifumi NISHIO |
3rd Author's Affiliation | Department of Electrical and Electronic Engineering, Tokushima University |
Date | 2013-10-28 |
Paper # | NLP2013-71 |
Volume (vol) | vol.113 |
Number (no) | 271 |
Page | pp.pp.- |
#Pages | 5 |
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