Abstract
This chapter develops the concept of the generalized Hough transform (GHT), which can be used to find all shapes, and shows how it is related to the spatial matched filter—thereby leading to optimal detection sensitivity. This means that gradient rather than uniform weighting should be used in parameter space. As an example, the chapter describes the application of the GHT to ellipse detection. It then compares the computational loads of three ellipse detection methods. The chapter also introduces the subject of abstract pattern matching, with particular reference to location of objects from point features. Because of variations caused by noise, clutter, and partial occlusions, object and image graphs need to be matched using subgraph–subgraph isomorphism: Unfortunately, the classic maximal clique approach is particularly computation intensive. However, in certain applications, this problem may be largely eliminated by employing the GHT, which is found to require far less computation.
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