The image edge detection is important tool of image processing and also is the foundation of pattern recognition and computer vision, which will affect the later processes. To noisy image, the edge detection is more important for noise is so common in image. This paper deeply studies the edge detection methods of the noisy images and concentrates on the order morphology method. By analysis and study the theory of order morphology, this paper constructs three edge detection operations and analyzes the specialities of these operations and the structure elements. Based on this, the new edge detection operations for noisy image based on multi-scale and multi-structuring element order morphology are proposed. The operations can obtain clear and exact edge of the noisy images. By simulation and comparing with the traditional edge detection operations and the order morphology operations, the operations are more effective on noise restraining and retaining the image details.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Noisy Image Edge Detection Based on Multi-scale and Multi-structuring Element Order Morphology Transformation


    Contributors:
    Xu, Yanlei (author) / Zhao, Jiyin (author) / Jiao, Yubin (author)


    Publication date :

    2008-05-01


    Size :

    573669 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Rank-Order and Morphological Image Processing with Adaptive Structuring Element

    Kober, V. I. / Mozerov, M. I. / Alvarez-Borrego, J. et al. | British Library Online Contents | 2003



    Edge detection based on order morphology filtering [4550-55]

    Ye, B. / Peng, J. | British Library Conference Proceedings | 2001


    Grayscale image contrast enhancement based on multi-scale edge representation

    Zhai, Guangtao / Zhang, Wenjun / Yang, Xiaokang | SPIE | 2005


    A statistical approach to multi-scale edge detection

    Konishi, S. / Yuille, A. / Coughlan, J. | British Library Online Contents | 2003