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  • Pavement crack detection based on saliency and statistical features

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    Author(s)
    Xu, Wei
    Tang, Zhenmin
    Zhou, Jun
    Ding, Jundi
    Griffith University Author(s)
    Zhou, Jun
    Year published
    2013
    Metadata
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    Abstract
    Traditional pavement crack detection methods can not cope well with the complexity and diversity of noises in large image area. To solve this problem, we propose a novel unsupervised crack detection approach based on saliency and statistical features. The saliency is initially represented by a conspicuity map built from the intensity rarity and local contrast of image regions. Then spatial continuity of candidate crack pixels is measured based on the statistical features extracted in their neighborhood. This is followed by a Bayesian model to automatically update the saliency map. Finally, cracks are extracted after adaptive ...
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    Traditional pavement crack detection methods can not cope well with the complexity and diversity of noises in large image area. To solve this problem, we propose a novel unsupervised crack detection approach based on saliency and statistical features. The saliency is initially represented by a conspicuity map built from the intensity rarity and local contrast of image regions. Then spatial continuity of candidate crack pixels is measured based on the statistical features extracted in their neighborhood. This is followed by a Bayesian model to automatically update the saliency map. Finally, cracks are extracted after adaptive saliency map binarization. Experiments show that proposed method has generated consistent results as those by human visual inspection. The results have also proved the effectiveness of the proposed method in suppressing noises compared with several alternative methods.
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    Conference Title
    2013 20TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2013)
    DOI
    https://doi.org/10.1109/ICIP.2013.6738843
    Copyright Statement
    © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Computer vision
    Publication URI
    http://hdl.handle.net/10072/57177
    Collection
    • Conference outputs

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