Templates and the Hough Transform

Shape, in both 2 and 3D, provides a primary cue for object recognition and the Hough Transform method has received considerable attention as a shape analysis technique. The relationship between the Hough Transform and template matching has long been recognised but its exploitation has been much neglected. In this paper we introduce a novel result which relates the quantization of the Hough parameter space and image template shapes. We show how the result can be used to construct equivalent feature space templates and demonstrate that this analysis yields valuable information concerning the design of Hough Transforms. The template interpretation explains why several recent hierarchical algorithms for efficient Hough Transform implementation may fail in complex imagery and it gives insight into several other aspects (e.g. filtering) of proposed Hough transform methods.