This paper presents a novel method for edge detection based on multiscale wavelet features and fuzzy c-means clustering. Firstly, an effective feature extraction algorithm using multiscale wavelet transform was proposed to extract classification features, thus the feature vector for each pixel was gained, which contained the gradient information in various directions; and then, these vectors were used as inputs for the fuzzy c-means clustering algorithm, which resulted in an automatic classification. In this way, the edge map can be obtained adaptively. Some comparisons with traditional edge detection algorithms were given in this paper. Experimental results demonstrated that the proposed method had a more satisfying performance.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multiscale edge detection based on fuzzy c-means clustering


    Contributors:
    Yishu Zhai, (author) / Xiaoming Liu, (author)


    Publication date :

    2006-01-01


    Size :

    3398824 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Application Study on Fuzzy Multiscale Edge Detection Algorithm

    Hou, B.-p. / Zhi-huan, S. / Li, P. | British Library Online Contents | 2005


    Fuzzy Multiscale Edge Detection (FMED) Applied to Automatic Left Ventricle Boundary Extraction

    Soraghan, J. J. / Setarehdan, K. / Institution of Electrical Engineers et al. | British Library Conference Proceedings | 1999


    Circle detection based on multiscale edge curvature estimation

    Semeikina, E. V. / Yurin, D. V. | British Library Online Contents | 2011


    Morphological pyramids for multiscale edge detection

    Wei Chen / Acton, S.T. | IEEE | 1998


    Morphological Pyramids for Multiscale Edge Detection

    Chen, W. / Acton, S. / University of Arizona et al. | British Library Conference Proceedings | 1998