Title :
Efficient Tensor Voting with 3D tensorial harmonics
Author :
Reisert, Marco ; Burkhardt, Hans
Author_Institution :
Albert Ludwig Univ. Georges, Freiburg
Abstract :
Tensor voting is a robust technique to extract low-level features in noisy images. The approach achieves its robustness by exploiting coherent orientations in local neighborhoods. In this paper we propose an efficient algorithm for dense tensor voting in 3D which makes use of steerable filters. Therefore, we propose steerable expansions of spherical tensor fields in terms of tensorial harmonics, which are their canonical representation. In this way it is possible to perform arbitrary rank tensor voting by linear-combinations of convolutions in an efficient way.
Keywords :
feature extraction; image denoising; tensors; 3D tensorial harmonics; efficient tensor voting; low-level feature extraction; noisy images; steerable filters; Anisotropic magnetoresistance; Data mining; Feature extraction; Harmonic analysis; Nonlinear filters; Power harmonic filters; Robustness; TV; Tensile stress; Voting;
Conference_Titel :
Computer Vision and Pattern Recognition Workshops, 2008. CVPRW '08. IEEE Computer Society Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
978-1-4244-2339-2
Electronic_ISBN :
2160-7508
DOI :
10.1109/CVPRW.2008.4562962