Titlebook: Shape Detection in Computer Vision Using the Hough Transform; V. F. Leavers Textbook 1992 Springer-Verlag London Limited 1992 Hough transf

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Transforms Without Tears,a good way of partitioning points on the boundary of a shape (Edge Image Data) into sets. Each set is then labelled by the parameters associated with the shape formed by those points. In order to be able to put such a theoretically powerful technique to good use it is necessary not just to ‘know’ ab

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Postprocessing,ing in order to extract suitable data for input to the transformation. We have dealt with programming a simple Hough algorithm and have thought a little about the quantisation in the transform space. Armed with the means to execute the transformation we are ready to press on with our task of shape d

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Which Hough?, available literature. An excellent comprehensive review of available methods up to and including 1987 is given by Illingworth [Illingworth et al 1988]. From such a review we can see that there are many different kinds of Hough Transform. It is difficult to know how to begin to categorize them. In t

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A Case Study: Circles and Ellipses,that the majority of effort is directed towards straight line detection. Such implementations can seldom be generalized to include the detection of shapes having more than two defining parameters. Moreover, the detection of more complicated shapes is rarely dealt with other than by the use of softwa

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A Case Study: Circles and Ellipses,re solutions. The Probabilistic Houghs offer a unique opportunity for generalization to . dimensions. In particular, the Dynamic Generalized Hough Transform has an algorithm that is uniquely suited to parallel implementation irrespective of the number of parameters under detection.

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