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  • Fractional Discrimination for Texture Image Segmentation

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    6164_1.pdf (368.5Kb)
    Author(s)
    You, J
    Hungnahally, S
    Sattar, A
    Griffith University Author(s)
    Sattar, Abdul
    Year published
    1997
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    Abstract
    Texture image segmentation plays an important role in texture analysis. This paper presents an approach to image segmentation by texture classification based on fractional discrimination functions. The idea behind this method is to enhance the texture edge points by means of image decomposition and contextual filtering in terms of the proposed fractional function. In addition, such function is described in a unified form with three-parameters. The parameters determine the global scale in conjunction with local scales for feature identification. Our experimental results show that texture features can be effectively ...
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    Texture image segmentation plays an important role in texture analysis. This paper presents an approach to image segmentation by texture classification based on fractional discrimination functions. The idea behind this method is to enhance the texture edge points by means of image decomposition and contextual filtering in terms of the proposed fractional function. In addition, such function is described in a unified form with three-parameters. The parameters determine the global scale in conjunction with local scales for feature identification. Our experimental results show that texture features can be effectively extracted on the basis of the selective fractional discrimination function.
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    Conference Title
    INTERNATIONAL CONFERENCE ON IMAGE PROCESSING - PROCEEDINGS, VOL I
    Volume
    1
    Publisher URI
    http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4998
    DOI
    https://doi.org/10.1109/ICIP.1997.647743
    Copyright Statement
    © 1997 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
    Publication URI
    http://hdl.handle.net/10072/19802
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    • Conference outputs

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