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High-dimensional statistical distance for region-of-interest tracking: Application to combining a soft geometric constraint with

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High-dimensional statistical distance for region-of-interest tracking: Application to combining a soft geometric constraint with
This paper deals with region-of-interest (ROI) tracking in video sequences. The goal is to determine in successive frames the region which best matches, in terms of a similarity measure, an ROI defined in a reference frame. Two aspects of a similarity measure between a reference region and a candidate region can be distinguished: radiometry which checks if the regions have similar colors and geometry which checks if these colors appear at the same location in the regions. Measures based solely on radiometry include distances between probability density functions (PDF) of color. The absence of geometric constraint increases the number of potential matches. A soft geometric constraint can be added to a PDF-based measure by enriching the color information with location, thus increasing the dimension of the domain of definition of the PDFs. However, high-dimensional PDF estimation is not trivial. Instead, we propose to compute the Kullback-Leibler distance between high-dimensional PDFs wi...
Sylvain Boltz, Eric Debreuve, Michel Barlaud
Added 12 Oct 2009
Updated 28 Feb 2011
Type Conference
Year 2007
Where CVPR
Authors Sylvain Boltz, Eric Debreuve, Michel Barlaud
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