Image Processing Based on Percolation Model
Summary :
This paper proposes a novel image processing method based on a percolation model. The percolation model is used to represent the natural phenomenon of the permeation of liquid. The percolation takes into account the connectivity among the neighborhoods. In the proposed method, a cluster formation by the percolation process is performed first. Then, feature extraction from the cluster is carried out. Therefore, this method is a type of scalable window processing for realizing a robust and flexible feature extraction. The effectiveness of proposed method was verified by experiments on crack detection, noise reduction, and edge detection.
- Publication
- IEICE TRANSACTIONS on Information Vol.E89-D No.7 pp.2044-2052
- Publication Date
- 2006/07/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e89-d.7.2044
- Type of Manuscript
- Special Section PAPER (Special Section on Machine Vision Applications)
- Category
- Feature Extraction
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Tomoyuki YAMAGUCHI, Shuji HASHIMOTO, "Image Processing Based on Percolation Model" in IEICE TRANSACTIONS on Information,
vol. E89-D, no. 7, pp. 2044-2052, July 2006, doi: 10.1093/ietisy/e89-d.7.2044.
Abstract: This paper proposes a novel image processing method based on a percolation model. The percolation model is used to represent the natural phenomenon of the permeation of liquid. The percolation takes into account the connectivity among the neighborhoods. In the proposed method, a cluster formation by the percolation process is performed first. Then, feature extraction from the cluster is carried out. Therefore, this method is a type of scalable window processing for realizing a robust and flexible feature extraction. The effectiveness of proposed method was verified by experiments on crack detection, noise reduction, and edge detection.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e89-d.7.2044/_p
Copy
@ARTICLE{e89-d_7_2044,
author={Tomoyuki YAMAGUCHI, Shuji HASHIMOTO, },
journal={IEICE TRANSACTIONS on Information},
title={Image Processing Based on Percolation Model},
year={2006},
volume={E89-D},
number={7},
pages={2044-2052},
abstract={This paper proposes a novel image processing method based on a percolation model. The percolation model is used to represent the natural phenomenon of the permeation of liquid. The percolation takes into account the connectivity among the neighborhoods. In the proposed method, a cluster formation by the percolation process is performed first. Then, feature extraction from the cluster is carried out. Therefore, this method is a type of scalable window processing for realizing a robust and flexible feature extraction. The effectiveness of proposed method was verified by experiments on crack detection, noise reduction, and edge detection.},
keywords={},
doi={10.1093/ietisy/e89-d.7.2044},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - Image Processing Based on Percolation Model
T2 - IEICE TRANSACTIONS on Information
SP - 2044
EP - 2052
AU - Tomoyuki YAMAGUCHI
AU - Shuji HASHIMOTO
PY - 2006
DO - 10.1093/ietisy/e89-d.7.2044
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E89-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2006
AB - This paper proposes a novel image processing method based on a percolation model. The percolation model is used to represent the natural phenomenon of the permeation of liquid. The percolation takes into account the connectivity among the neighborhoods. In the proposed method, a cluster formation by the percolation process is performed first. Then, feature extraction from the cluster is carried out. Therefore, this method is a type of scalable window processing for realizing a robust and flexible feature extraction. The effectiveness of proposed method was verified by experiments on crack detection, noise reduction, and edge detection.
ER -
English
