The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inferring global perceptual contours from local features
International Journal of Computer Vision - Special issue on computer vision research at the University of Southern California
Computational Framework for Segmentation and Grouping
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Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
Image and Vision Computing
From stochastic completion fields to tensor voting
DSSCV'05 Proceedings of the First international conference on Deep Structure, Singularities, and Computer Vision
Noisy data reduction by using tensor and fuzzy c-means algorithm
ISCGAV'07 Proceedings of the 7th WSEAS International Conference on Signal Processing, Computational Geometry & Artificial Vision
Graph-based geometric-iconic guide-wire tracking
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Detection of electrophysiology catheters in noisy fluoroscopy images
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
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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.