IEICE TRANSACTIONS on Fundamentals
Improving Image Segmentation by Chaotic Neurodynamics
Summary :
We propose a novel segmentation algorithm which combines an image segmentation method into small regions with chaotic neurodynamics that has already been clarified to be effective for solving some combinatorial optimization problems. The basic algorithm of an image segmentation is the variable-shape-bloch-segmentation (VB) which searches an opti-mal state of the segmentation by moving the vertices of quadran-gular regions. However, since the algorithm for moving vertices is based upon steepest descent dynamics, this segmentation method has a local minimum problem that the algorithm gets stuck at undesirable local minima. In order to treat such a problem of the VB and improve its performance, we introduce chaotic neurodynamics for optimization. The results of our novel method are compared with those of conventional stochastic dynamics for escaping from undesirable local minima. As a result, the better results are obtained with the chaotic neurodynamical image segmentation.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E79-A No.10 pp.1630-1637
- Publication Date
- 1996/10/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Section on Nonlinear Theory and its Applications (NOLTA))
- Category
- Neural Nets and Human Being
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Mikio HASEGAWA, Tohru IKEGUCHI, Takeshi MATOZAKI, Kazuyuki AIHARA, "Improving Image Segmentation by Chaotic Neurodynamics" in IEICE TRANSACTIONS on Fundamentals,
vol. E79-A, no. 10, pp. 1630-1637, October 1996, doi: .
Abstract: We propose a novel segmentation algorithm which combines an image segmentation method into small regions with chaotic neurodynamics that has already been clarified to be effective for solving some combinatorial optimization problems. The basic algorithm of an image segmentation is the variable-shape-bloch-segmentation (VB) which searches an opti-mal state of the segmentation by moving the vertices of quadran-gular regions. However, since the algorithm for moving vertices is based upon steepest descent dynamics, this segmentation method has a local minimum problem that the algorithm gets stuck at undesirable local minima. In order to treat such a problem of the VB and improve its performance, we introduce chaotic neurodynamics for optimization. The results of our novel method are compared with those of conventional stochastic dynamics for escaping from undesirable local minima. As a result, the better results are obtained with the chaotic neurodynamical image segmentation.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e79-a_10_1630/_p
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@ARTICLE{e79-a_10_1630,
author={Mikio HASEGAWA, Tohru IKEGUCHI, Takeshi MATOZAKI, Kazuyuki AIHARA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Improving Image Segmentation by Chaotic Neurodynamics},
year={1996},
volume={E79-A},
number={10},
pages={1630-1637},
abstract={We propose a novel segmentation algorithm which combines an image segmentation method into small regions with chaotic neurodynamics that has already been clarified to be effective for solving some combinatorial optimization problems. The basic algorithm of an image segmentation is the variable-shape-bloch-segmentation (VB) which searches an opti-mal state of the segmentation by moving the vertices of quadran-gular regions. However, since the algorithm for moving vertices is based upon steepest descent dynamics, this segmentation method has a local minimum problem that the algorithm gets stuck at undesirable local minima. In order to treat such a problem of the VB and improve its performance, we introduce chaotic neurodynamics for optimization. The results of our novel method are compared with those of conventional stochastic dynamics for escaping from undesirable local minima. As a result, the better results are obtained with the chaotic neurodynamical image segmentation.},
keywords={},
doi={},
ISSN={},
month={October},}
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TY - JOUR
TI - Improving Image Segmentation by Chaotic Neurodynamics
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1630
EP - 1637
AU - Mikio HASEGAWA
AU - Tohru IKEGUCHI
AU - Takeshi MATOZAKI
AU - Kazuyuki AIHARA
PY - 1996
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E79-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 1996
AB - We propose a novel segmentation algorithm which combines an image segmentation method into small regions with chaotic neurodynamics that has already been clarified to be effective for solving some combinatorial optimization problems. The basic algorithm of an image segmentation is the variable-shape-bloch-segmentation (VB) which searches an opti-mal state of the segmentation by moving the vertices of quadran-gular regions. However, since the algorithm for moving vertices is based upon steepest descent dynamics, this segmentation method has a local minimum problem that the algorithm gets stuck at undesirable local minima. In order to treat such a problem of the VB and improve its performance, we introduce chaotic neurodynamics for optimization. The results of our novel method are compared with those of conventional stochastic dynamics for escaping from undesirable local minima. As a result, the better results are obtained with the chaotic neurodynamical image segmentation.
ER -
English
