IEICE TRANSACTIONS on Fundamentals
Fuzzy Rule-Based Edge Detection Using Multiscale Edge Images
Summary :
Fuzzy rule-based edge detection using multiscale edge images is proposed. In this method, the edge image is obtained by fuzzy approximate reasoning from multiscale edge images which are obtained by derivative operators with various window sizes. The effect of utilizing multiscale edge images for edge detection is already known, but how to design the rules for deciding edges from multiscale edge images is not clarified yet. In this paper, the rules are represented in a fuzzy style, since edges are usually defined ambiguously, and the fuzzy rules are designed optimally by a training method. Here, the fuzzy approximate reasoning is expressed as a nonlinear function of the multiscale edge image data, and the nonlinear function is optimized so that the mean square error of the edge detection be the minimum. Computer simulations verify its high performance for actual images.
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- IEICE TRANSACTIONS on Fundamentals Vol.E83-A No.2 pp.291-300
- Publication Date
- 2000/02/25
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- Special Section PAPER (Special Section on Intelligent Signal and Image Processing)
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Kaoru ARAKAWA, "Fuzzy Rule-Based Edge Detection Using Multiscale Edge Images" in IEICE TRANSACTIONS on Fundamentals,
vol. E83-A, no. 2, pp. 291-300, February 2000, doi: .
Abstract: Fuzzy rule-based edge detection using multiscale edge images is proposed. In this method, the edge image is obtained by fuzzy approximate reasoning from multiscale edge images which are obtained by derivative operators with various window sizes. The effect of utilizing multiscale edge images for edge detection is already known, but how to design the rules for deciding edges from multiscale edge images is not clarified yet. In this paper, the rules are represented in a fuzzy style, since edges are usually defined ambiguously, and the fuzzy rules are designed optimally by a training method. Here, the fuzzy approximate reasoning is expressed as a nonlinear function of the multiscale edge image data, and the nonlinear function is optimized so that the mean square error of the edge detection be the minimum. Computer simulations verify its high performance for actual images.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e83-a_2_291/_p
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@ARTICLE{e83-a_2_291,
author={Kaoru ARAKAWA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Fuzzy Rule-Based Edge Detection Using Multiscale Edge Images},
year={2000},
volume={E83-A},
number={2},
pages={291-300},
abstract={Fuzzy rule-based edge detection using multiscale edge images is proposed. In this method, the edge image is obtained by fuzzy approximate reasoning from multiscale edge images which are obtained by derivative operators with various window sizes. The effect of utilizing multiscale edge images for edge detection is already known, but how to design the rules for deciding edges from multiscale edge images is not clarified yet. In this paper, the rules are represented in a fuzzy style, since edges are usually defined ambiguously, and the fuzzy rules are designed optimally by a training method. Here, the fuzzy approximate reasoning is expressed as a nonlinear function of the multiscale edge image data, and the nonlinear function is optimized so that the mean square error of the edge detection be the minimum. Computer simulations verify its high performance for actual images.},
keywords={},
doi={},
ISSN={},
month={February},}
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TY - JOUR
TI - Fuzzy Rule-Based Edge Detection Using Multiscale Edge Images
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 291
EP - 300
AU - Kaoru ARAKAWA
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E83-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2000
AB - Fuzzy rule-based edge detection using multiscale edge images is proposed. In this method, the edge image is obtained by fuzzy approximate reasoning from multiscale edge images which are obtained by derivative operators with various window sizes. The effect of utilizing multiscale edge images for edge detection is already known, but how to design the rules for deciding edges from multiscale edge images is not clarified yet. In this paper, the rules are represented in a fuzzy style, since edges are usually defined ambiguously, and the fuzzy rules are designed optimally by a training method. Here, the fuzzy approximate reasoning is expressed as a nonlinear function of the multiscale edge image data, and the nonlinear function is optimized so that the mean square error of the edge detection be the minimum. Computer simulations verify its high performance for actual images.
ER -
English
