An Efficient Method for Tensor Voting Using Steerable Filters

  • Erik Franken
  • Markus van Almsick
  • Peter Rongen
  • Luc Florack
  • Bart ter Haar Romeny
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3954)


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.


Graphical Processing Unit Noisy Image Graphical Processing Unit Implementation Local Image Feature Tensor Vote 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Erik Franken
    • 1
  • Markus van Almsick
    • 1
  • Peter Rongen
    • 2
  • Luc Florack
    • 1
  • Bart ter Haar Romeny
    • 1
  1. 1.Department of Biomedical EngineeringTechnische Universiteit EindhovenEindhovenThe Netherlands
  2. 2.Philips Medical Systems, BestThe Netherlands

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