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Abstract for wright_thesis95

PhD Thesis


Mark Wright

June 1995

Shape representation has always played a central role in computer vision. Skeletal shape descriptors which make symmetry explicit are an important class of shape representations. The goal of this thesis is to study the problems encountered using skeletal shape descriptors. The thesis unites three main themes of work: A filter based approach to skeletonisation, skeletonisation using parallel wave propagation and skeletonisation using an extended Euclidean distance transform.

The distance transform approach to skeletonisation computes a skeleton by identifying the so called local maxima of the distance transform. A new method has been proposed to detect these features using a filter-base approach inspired by models of processes in the human visual system. Further improvements were made by using a filter designed to detect a specific geometric feature on the distance transform which corresponded to the skeletal points. This improved the quality of skeletons obtained but could only compute restricted skeletal descriptions.

The wave propagation algorithm of Brady and Scott has been studied; they originally implemented this on a simulator of the Connection Machine. The issues of mapping the algorithm onto an array of transputers have been investigated. An efficient implementation was realised by reducing synchronisation and data transfer overheads. It was found that the algorithm could compute more general shape descriptions than the distance transform approach but the quality of skeletons produced was not as good.

Using standard techniques from singularity theory, an analysis of distance functions from object boundaries has been undertaken. This resulted in the formal definition of a new extended Euclidean distance transform. An algorithm has been devised to perform skeletonisation using the extended distance transform. This combined the advantages of the filter and wave based techniques in that it produced skeletons of a high quality which made more symmetries explicit than the standard distance transform approach. In addition, the extended distance transform provides an elegant unifying framework for work on skeletal shape descriptions.

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