Contrast Marginalised Gradient Template Matching
This paper addresses a key problem in the detection of shapes via template matching: the variation of accumulator-space response with object-background contrast. By formulating a probabilistic model for planar shape location within an image or video frame, a vector-field filtering operation may be derived which, in the limiting case of vanishing noise, leads to the Hough-transform filters reported by Kerbyson & Atherton . By further incorporating a model for contrast uncertainty, a contrast invariant accumulator space is constructed, in which local maxima provide an indication of the most probable locations of a sought planar shape. Comparisons with correlation matching, and Hough transforms employing gradient magnitude, binary and vector templates are presented. A key result is that a posterior density function for locating a shape marginalised for contrast uncertainty is obtained by summing the functions of the outputs of a series of spatially invariant filters, thus providing a route to fast parallel implementations.
KeywordsProbability Density Function Template Match Hough Transform Planar Shape Posterior Density Function
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