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  • Object Detection via Foreground Contour Feature Selection and Part-based Shape Model

    Author
    Huigang, Zhang
    Wang, Junxiu
    Xiao, Bai
    Zhou, Jun
    Jian, Cheng
    Huijie, Zhao
    Year published
    2012
    Metadata
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    Abstract
    In this paper, we propose a novel approach for object detection via foreground feature selection and part-based shape model. It automatically learns a shape model from cluttered training images without need to explicitly given bounding box on objects. Our approach commences by extracting a set of feature descriptors, and iteratively selects the foreground features using Earth Movers Distances based matching. This leads to a part-based shape model that can be used for object detection. Experimental results show that the proposed method has comparable performance with the state-of-the-art shape-based detection methods but with ...
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    In this paper, we propose a novel approach for object detection via foreground feature selection and part-based shape model. It automatically learns a shape model from cluttered training images without need to explicitly given bounding box on objects. Our approach commences by extracting a set of feature descriptors, and iteratively selects the foreground features using Earth Movers Distances based matching. This leads to a part-based shape model that can be used for object detection. Experimental results show that the proposed method has comparable performance with the state-of-the-art shape-based detection methods but with less requirements on the data at the training stage.
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    Conference Title
    21st International Conference on Pattern Recognition (ICPR) 2012
    Publisher URI
    http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6460681
    Subject
    Pattern Recognition and Data Mining
    Computer Vision
    Publication URI
    http://hdl.handle.net/10072/52236
    Collection
    • Conference outputs

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