Automatic scale detection for contour fragment based on difference of curvature

Kei Kawamura, Daisuke Ishii, Hiroshi Watanabe

    Research output: Contribution to journalArticle

    Abstract

    Scale-invariant features are widely used for image retrieval and shape classification. The curvature of a planar curve is a fundamental feature and it is geometrically invariant with respect it the coordinate system. The curvature-based feature varies in position when multiscale analysis is performed. Therefore, it is important to recognize the scale in order to detect the feature point. Numerous shape descriptors based on contour shapes have been developed in the field of pattern recognition and computer vision. A curvature scale-space (CSS) representation cannot be applied to a contour fragment and requires the tracking of feature points. In a gradient-based curvature computation, although the gradient computation considers the scale, the curvature is normalized with respect to not the scale but the contour length. The scale-invariant feature transform algorithm that detects feature points from an image solves similar problems by using the difference of Gaussian (DoG). It is difficult to apply the SIFT algorithm to a planar curve for feature extraction. In this paper, an automatic scale detection method for a contour fragment is proposed. The proposed method detects the appropriate scales and their positions on the basis of the difference of curvature (DoC) without the tracking of feature points. To calculate the differences, scale-normalized curvature is introduced. An advantage of the DoC algorithm is that the appropriate scale can be obtained from a contour fragment as a local feature. It then extends the application area. The validity of the proposed method is confirmed by experiments. The proposed method provides the most stable and robust scales of feature points among conventional methods such as curvature scale-space and gradient-based curvature.

    Original languageEnglish
    Pages (from-to)1998-2005
    Number of pages8
    JournalIEICE Transactions on Information and Systems
    VolumeE94-D
    Issue number10
    DOIs
    Publication statusPublished - 2011 Oct

    Fingerprint

    Image retrieval
    Computer vision
    Pattern recognition
    Feature extraction
    Experiments

    Keywords

    • Curvature
    • Pattern recognition
    • Planar curve
    • Scale invariance
    • Shape description

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering
    • Software
    • Artificial Intelligence
    • Hardware and Architecture
    • Computer Vision and Pattern Recognition

    Cite this

    Automatic scale detection for contour fragment based on difference of curvature. / Kawamura, Kei; Ishii, Daisuke; Watanabe, Hiroshi.

    In: IEICE Transactions on Information and Systems, Vol. E94-D, No. 10, 10.2011, p. 1998-2005.

    Research output: Contribution to journalArticle

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