Image processing based on percolation model

Tomoyuki Yamaguchi, Shuji Hashimoto

    Research output: Contribution to journalArticle

    30 Citations (Scopus)

    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.

    Original languageEnglish
    Pages (from-to)2044-2052
    Number of pages9
    JournalIEICE Transactions on Information and Systems
    VolumeE89-D
    Issue number7
    DOIs
    Publication statusPublished - 2006 Jul

    Fingerprint

    Feature extraction
    Image processing
    Crack detection
    Edge detection
    Noise abatement
    Permeation
    Liquids
    Processing
    Experiments

    Keywords

    • Cluster formation
    • Feature extraction
    • Percolation
    • Scalable window

    ASJC Scopus subject areas

    • Information Systems
    • Computer Graphics and Computer-Aided Design
    • Software

    Cite this

    Image processing based on percolation model. / Yamaguchi, Tomoyuki; Hashimoto, Shuji.

    In: IEICE Transactions on Information and Systems, Vol. E89-D, No. 7, 07.2006, p. 2044-2052.

    Research output: Contribution to journalArticle

    Yamaguchi, Tomoyuki ; Hashimoto, Shuji. / Image processing based on percolation model. In: IEICE Transactions on Information and Systems. 2006 ; Vol. E89-D, No. 7. pp. 2044-2052.
    @article{60928ef493b5449a9947f1c8cfc9d70f,
    title = "Image processing based on percolation model",
    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 = "Cluster formation, Feature extraction, Percolation, Scalable window",
    author = "Tomoyuki Yamaguchi and Shuji Hashimoto",
    year = "2006",
    month = "7",
    doi = "10.1093/ietisy/e89-d.7.2044",
    language = "English",
    volume = "E89-D",
    pages = "2044--2052",
    journal = "IEICE Transactions on Information and Systems",
    issn = "0916-8532",
    publisher = "Maruzen Co., Ltd/Maruzen Kabushikikaisha",
    number = "7",

    }

    TY - JOUR

    T1 - Image processing based on percolation model

    AU - Yamaguchi, Tomoyuki

    AU - Hashimoto, Shuji

    PY - 2006/7

    Y1 - 2006/7

    N2 - 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.

    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.

    KW - Cluster formation

    KW - Feature extraction

    KW - Percolation

    KW - Scalable window

    UR - http://www.scopus.com/inward/record.url?scp=33747876948&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=33747876948&partnerID=8YFLogxK

    U2 - 10.1093/ietisy/e89-d.7.2044

    DO - 10.1093/ietisy/e89-d.7.2044

    M3 - Article

    AN - SCOPUS:33747876948

    VL - E89-D

    SP - 2044

    EP - 2052

    JO - IEICE Transactions on Information and Systems

    JF - IEICE Transactions on Information and Systems

    SN - 0916-8532

    IS - 7

    ER -