Presentation 2000/3/18
Angle-Dependent Image Filter using Cellular Neural Networks
Hector SANDOVAL, Taizoh HATTORI, Sachiko KITAGAWA, Yasutami CHIGUSA,
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Abstract(in English) The importance of edge detection is, nowadays, leading to the development of improved edge detection methods. In this paper we described a novel image filter that produces an angle-dependent edge detection followed by a thresholding stage and, finally, by a thinning stage. In the angle-dependent edge detection stage, we show how, applying an alternative method, can be obtained a pair of edge detectors that are orthogonal between them. Modified threshold and thinning methods are included to improve the quality of the edge detected image. We also show how the entire filter could be implemented as a Cellular Neural Network (CNN). Thus, the system inherits all the dynamics as well as the energy and stability characteristics of a CNN. Some necessary background is first introduced. Our preliminary simulations as well as preliminary results, are presented. An evaluation of the results obtained is included along with suggestions for further research.
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Keyword(in English) CNN / edge detection / image filter / image thinning
Paper # NLP99-175
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Conference Information
Committee NLP
Conference Date 2000/3/18(1days)
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Registration To Nonlinear Problems (NLP)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Angle-Dependent Image Filter using Cellular Neural Networks
Sub Title (in English)
Keyword(1) CNN
Keyword(2) edge detection
Keyword(3) image filter
Keyword(4) image thinning
1st Author's Name Hector SANDOVAL
1st Author's Affiliation Department of Information Technology Tokyo University of Technology()
2nd Author's Name Taizoh HATTORI
2nd Author's Affiliation Tokyo International University
3rd Author's Name Sachiko KITAGAWA
3rd Author's Affiliation Department of Information Technology Tokyo University of Technology
4th Author's Name Yasutami CHIGUSA
4th Author's Affiliation Department of Information Technology Tokyo University of Technology
Date 2000/3/18
Paper # NLP99-175
Volume (vol) vol.99
Number (no) 715
Page pp.pp.-
#Pages 8
Date of Issue