Performance of optical flow techniques
International Journal of Computer Vision
Variational Optic Flow Computation with a Spatio-Temporal Smoothness Constraint
Journal of Mathematical Imaging and Vision
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
An Energy Minimisation Approach to Stereo-Temporal Dense Reconstruction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
High performance imaging using large camera arrays
ACM SIGGRAPH 2005 Papers
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Edge-preserving Simultaneous Joint Motion-Disparity Estimation
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Symmetrical Dense Optical Flow Estimation with Occlusions Detection
International Journal of Computer Vision
Particle Video: Long-Range Motion Estimation Using Point Trajectories
International Journal of Computer Vision
A Statistical Confidence Measure for Optical Flows
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Efficient Dense Scene Flow from Sparse or Dense Stereo Data
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Reconstructing Optical Flow Fields by Motion Inpainting
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
An adaptive confidence measure for optical flows based on linear subspace projections
Proceedings of the 29th DAGM conference on Pattern recognition
A duality based approach for realtime TV-L1 optical flow
Proceedings of the 29th DAGM conference on Pattern recognition
Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bilateral filtering-based optical flow estimation with occlusion detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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Many applications in computer vision and computer graphics require dense correspondences between images of multi-view video streams. Most state-of-the-art algorithms estimate correspondences by considering pairs of images. However, in multi-view videos, several images capture nearly the same scene. In this article we show that this redundancy can be exploited to estimate more robust and consistent correspondence fields. We use the multi-video data structure to establish a confidence measure based on the consistency of the correspondences in a loop of three images. This confidence measure can be applied after flow estimation is terminated to find the pixels for which the estimate is reliable. However, including the measure directly into the estimation process yields dense and highly accurate correspondence fields. Additionally, application of the loop consistency confidence measure allows us to include sparse feature matches directly into the dense optical flow estimation. With the confidence measure, spurious matches can be successfully suppressed during optical flow estimation while correct matches contribute to increase the accuracy of the flow.