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An efficient method for tensor voting using steerable filters

By EM Erik Franken, MA Markus van Almsick, PMJ Peter Rongen, LMJ Luc Florack and BM Bart ter Haar Romeny


In many image analysis applications there is a need to extract curves in noisy images. To achieve a more robust extraction, one can exploit correlations of oriented features over a spatial context in the image. Tensor voting is an existing technique to extract features in this way. In this paper, we present a new computational scheme for tensor voting on a dense field of rank-2 tensors. Using steerable filter theory, it is possible to rewrite the tensor voting operation as a linear combination of complex-valued convolutions. This approach has computational advantages since convolutions can be implemented efficiently. We provide speed measurements to indicate the gain in speed, and illustrate the use of steerable tensor voting on medical applications

Publisher: 'Springer Fachmedien Wiesbaden GmbH'
Year: 2006
DOI identifier: 10.1007/11744085_18
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Provided by: Repository TU/e
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