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    Robust ellipse detection with Gaussian mixture models

    Arellano, Claudia and Dahyot, Rozenn (2016) Robust ellipse detection with Gaussian mixture models. Pattern Recognition, 58. pp. 12-16. ISSN 0031-3203

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    The Euclidian distance between Gaussian Mixtures has been shown to be robust to perform point set registration (Jian and Vemuri, 2011). We propose to extend this idea for robustly matching a family of shapes (ellipses). Optimisation is performed with an annealing strategy, and the search for occurrences is repeated several times to detect multiple instances of the shape of interest. We compare experimentally our approach to other state-of-the-art techniques on a benchmark database for ellipses, and demonstrate the good performance of our approach.

    Item Type: Article
    Keywords: Ellipse detection; L2 distance; GMM; Parameter estimation;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 15108
    Identification Number:
    Depositing User: Rozenn Dahyot
    Date Deposited: 07 Dec 2021 16:35
    Journal or Publication Title: Pattern Recognition
    Publisher: Elsevier
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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