IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion detection in spatio-temporal space
Computer Vision, Graphics, and Image Processing
Multidimensional Orientation Estimation with Applications to Texture Analysis and Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance of optical flow techniques
International Journal of Computer Vision
The statistics of optical flow
Computer Vision and Image Understanding
A Theoretical Framework for Convex Regularizers in PDE-Based Computation of Image Motion
International Journal of Computer Vision
Variational Optic Flow Computation with a Spatio-Temporal Smoothness Constraint
Journal of Mathematical Imaging and Vision
On Functionals with Greyvalue-Controlled Smoothness Terms for Determining Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
Accurate Motion Flow Estimation with Discontinuities
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods
International Journal of Computer Vision
Towards Ultimate Motion Estimation: Combining Highest Accuracy with Real-Time Performance
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
International Journal of Computer Vision
Local Adaptivity to Variable Smoothness for Exemplar-Based Image Regularization and Representation
International Journal of Computer Vision
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Constraints for the estimation of displacement vector fields from image sequences
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
A Database and Evaluation Methodology for Optical Flow
International Journal of Computer Vision
Motion segmentation with accurate boundaries: a tensor voting approach
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
On Improving the Efficiency of Tensor Voting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge-preserving color image denoising through tensor voting
Computer Vision and Image Understanding
SimpleFlow: A Non-iterative, Sublinear Optical Flow Algorithm
Computer Graphics Forum
Modeling temporal coherence for optical flow
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Optical flow estimation using learned sparse model
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Differential optical flow methods allow the estimation of optical flow fields based on the first-order and even higher-order spatio-temporal derivatives (gradients) of sequences of input images. If the input images are noisy, for instance because of the limited quality of the capturing devices or due to poor illumination conditions, the use of partial derivatives will amplify that noise and thus end up affecting the accuracy of the computed flow fields. The typical approach in order to reduce that noise consists of smoothing the required gradient images with Gaussian filters, for instance by applying structure tensors. However, that filtering is isotropic and tends to blur the discontinuities that may be present in the original images, thus likely leading to an undesired loss of accuracy in the resulting flow fields. This paper proposes the use of tensor voting as an alternative to Gaussian filtering, and shows that the discontinuity preserving capabilities of the former yield more robust and accurate results. In particular, a state-of-the-art variational optical flow method has been adapted in order to utilize a tensor voting filtering approach. The proposed technique has been tested upon different datasets of both synthetic and real image sequences, and compared to both well known and state-of-the-art differential optical flow methods.