Abstract
This chapter shows how the Hough transform (HT) is able to locate circular and elliptic shapes in digital images. It starts by considering how circles can be located, and shows how the problem of unknown radius can be handled and how accuracy and speed can be improved. It goes on to consider ellipse detection, and describes two main methods—the diameter bisection method and the chord–tangent method; it also shows how the various parameters (five in all) can be estimated once an ellipse has been located. A means for confirming that a shape is an ellipse is also presented. The chapter ends with studies of hole detection and human iris location. Overall, the chapter emphasizes that the HT relies on the accumulation of votes in parameter space, which gives it considerable robustness against object fragmentation, distortion, and partial occlusion—ultimately because of the concentration on positive evidence for objects.
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