Statistical Template Matching under Geometric Transformations

9 years 3 months ago
Statistical Template Matching under Geometric Transformations
Abstract. We present a novel template matching framework for detecting geometrically transformed objects. A template is a simplified representation of the object of interest by a set of pixel groups of any shape, and the similarity between the template and an image region is derived from the F-test statistic. The method selects a geometric transformation from a discrete set of transformations, giving the best statistical independence of such groups Efficient matching is achieved using 1D analogue of integral images - integral lines, and the number of operations required to compute the matching score is linear with template size, comparing to quadratic dependency in conventional template matching. Although the assumption that the geometric deformation can be approximated from discrete set of transforms is restrictive, we introduce an adaptive subpixel refinement stage for accurate matching of object under arbitrary parametric 2D-transformation. The parameters maximizing the matching sco...
Alexander Sibiryakov
Added 19 Oct 2010
Updated 19 Oct 2010
Type Conference
Year 2008
Where DGCI
Authors Alexander Sibiryakov
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