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    A Model-Based Approach to Recovering the Structure of a Plant from Images

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    Ward et al 2015 - plant image analysis.pdf
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    Type
    Conference Paper
    Authors
    Ward, Ben
    Bastian, John
    van den Hengel, Anton
    Pooley, Daniel
    Bari, Rajendra
    Berger, Bettina
    Tester, Mark A. cc
    KAUST Department
    Biological and Environmental Science and Engineering (BESE) Division
    Center for Desert Agriculture
    Plant Science
    Plant Science Program
    The Salt Lab
    Date
    2015-03-19
    Preprint Posting Date
    2015-03-11
    Online Publication Date
    2015-03-19
    Print Publication Date
    2015
    Permanent link to this record
    http://hdl.handle.net/10754/556196
    
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    Abstract
    We present a method for recovering the structure of a plant directly from a small set of widely-spaced images for automated analysis of phenotype. Structure recovery is more complex than shape estimation, but the resulting structure estimate is more closely related to phenotype than is a 3D geometric model. The method we propose is applicable to a wide variety of plants, but is demonstrated on wheat. Wheat is composed of thin elements with few identifiable features, making it difficult to analyse using standard feature matching techniques. Our method instead analyses the structure of plants using only their silhouettes. We employ a generate-and-test method, using a database of manually modelled leaves and a model for their composition to synthesise plausible plant structures which are evaluated against the images. The method is capable of efficiently recovering accurate estimates of plant structure in a wide variety of imaging scenarios, without manual intervention.
    Citation
    Ward, B., Bastian, J., van den Hengel, A., Pooley, D., Bari, R., Berger, B., & Tester, M. (2015). A Model-Based Approach to Recovering the Structure of a Plant from Images. Lecture Notes in Computer Science, 215–230. doi:10.1007/978-3-319-16220-1_16
    Publisher
    Springer Nature
    Journal
    Computer Vision - ECCV 2014 Workshops
    Conference/Event name
    13th European Conference on Computer Vision, ECCV 2014
    DOI
    10.1007/978-3-319-16220-1_16
    arXiv
    1503.03191
    Additional Links
    http://link.springer.com/chapter/10.1007%2F978-3-319-16220-1_16
    http://arxiv.org/abs/1503.03191
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-319-16220-1_16
    Scopus Count
    Collections
    Conference Papers; Biological and Environmental Science and Engineering (BESE) Division; Plant Science Program; Center for Desert Agriculture

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