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  • Multiscale Contour Steered Region Integral and Its Application for Cultivar Classification

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    Author(s)
    Yu, Xiaohan
    Gao, Yongsheng
    Xiong, Shengwu
    Yuan, Xiaohui
    Griffith University Author(s)
    Yu, Xiaohan
    Gao, Yongsheng
    Year published
    2019
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    Abstract
    In this paper, a multiscale contour steered region integral (MCSRI) method is proposed to classify highly similar shapes with flexible interior connection architectures. A component distance map (CDM) is developed to robustly characterize the flexible interior connection structure, shape of the exterior contour, and their inter-relationship in a shape image. A novel multiscale region transform (MReT) is proposed to perform region integral over different contour-steered strips at all possible scales to effectively integrate patch features, and thus enables a better description of the shape image in a coarse-to-fine manner. ...
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    In this paper, a multiscale contour steered region integral (MCSRI) method is proposed to classify highly similar shapes with flexible interior connection architectures. A component distance map (CDM) is developed to robustly characterize the flexible interior connection structure, shape of the exterior contour, and their inter-relationship in a shape image. A novel multiscale region transform (MReT) is proposed to perform region integral over different contour-steered strips at all possible scales to effectively integrate patch features, and thus enables a better description of the shape image in a coarse-to-fine manner. It is applied to solve a challenging problem of classifying cultivars from leaf images, which is a new attempt in both biology and computer vision research communities. A soybean cultivar leaf vein database (SoyCultivarVein), which is the first cultivar leaf vein database, is created and presented for performance evaluation. The experimental results demonstrate the superiority of the proposed method over the state-of-the-art methods in similar shape classification and the possibility of cultivar recognition via leaf pattern analysis, which may lead to a new research interest towards fine-level shape analysis on cultivar classification.
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    Journal Title
    IEEE ACCESS
    Volume
    7
    DOI
    https://doi.org/10.1109/access.2019.2918263
    Copyright Statement
    © The Author(s) 2019. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
    Subject
    Information and Computing Sciences
    Engineering
    Technology
    Publication URI
    http://hdl.handle.net/10072/385956
    Collection
    • Journal articles

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