Abstract

This paper addresses a key problem in the detection of shapes via template matching: the variation of accumulator-space re- sponse with ob ject-background contrast. By formulating a probabilis- tic 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 [5]. By further incorporating a model for con- trast uncertainty, a contrast invariant accumulator space is constructed, in which local maxima provide an indication of the most probable loca- tions 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.