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    Modeling self-occlusions in dynamic shape and appearance tracking

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    Type
    Conference Paper
    Authors
    Yang, Yanchao
    Sundaramoorthi, Ganesh cc
    KAUST Department
    Electrical Engineering Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Visual Computing Center (VCC)
    Date
    2013-12
    Permanent link to this record
    http://hdl.handle.net/10754/564821
    
    Metadata
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    Abstract
    We present a method to track the precise shape of a dynamic object in video. Joint dynamic shape and appearance models, in which a template of the object is propagated to match the object shape and radiance in the next frame, are advantageous over methods employing global image statistics in cases of complex object radiance and cluttered background. In cases of complex 3D object motion and relative viewpoint change, self-occlusions and disocclusions of the object are prominent, and current methods employing joint shape and appearance models are unable to accurately adapt to new shape and appearance information, leading to inaccurate shape detection. In this work, we model self-occlusions and dis-occlusions in a joint shape and appearance tracking framework. Experiments on video exhibiting occlusion/dis-occlusion, complex radiance and background show that occlusion/dis-occlusion modeling leads to superior shape accuracy compared to recent methods employing joint shape/appearance models or employing global statistics. © 2013 IEEE.
    Citation
    Yang, Y., & Sundaramoorthi, G. (2013). Modeling Self-Occlusions in Dynamic Shape and Appearance Tracking. 2013 IEEE International Conference on Computer Vision. doi:10.1109/iccv.2013.32
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2013 IEEE International Conference on Computer Vision
    Conference/Event name
    2013 14th IEEE International Conference on Computer Vision, ICCV 2013
    ISBN
    9781479928392
    DOI
    10.1109/ICCV.2013.32
    ae974a485f413a2113503eed53cd6c53
    10.1109/ICCV.2013.32
    Scopus Count
    Collections
    Conference Papers; Electrical and Computer Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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