An Efficient Method for Tensor Voting Using Steerable Filters

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.

• Type: Conference
• Year: 2006
• Where: ECCV
• Authors: Erik Franken, Markus van Almsick, Peter Rongen, Luc Florack, Bart M. ter Haar Romeny
• Keywords: Computer Vision, ECCV 2006, Steerable Tensor, Tensor Voting, Tensor Voting Operation