An efficient method for tensor voting using steerable filters

Erik Franken, Markus van Almsick, Peter M. J. Rongen, Luc Florack, Bart M. ter Haar Romeny

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

45 Citations (Scopus)
1 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationComputer vision - ECCV 2006 : 9th European Conference on Computer Vision, Graz, Austria, May 7 - 13, 2006 ; proceedings
EditorsA. Leonardis, H Bischof, A. Pinz
Place of PublicationBerlin
Number of pages13
ISBN (Print)3-540-33838-1
Publication statusPublished - 2006
EventECCV 2006 : European Conference on Computer Vision ; 9 (Graz) - Graz
Duration: 5 Jul 200613 Jul 2006

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


ConferenceECCV 2006 : European Conference on Computer Vision ; 9 (Graz)


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