IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance of optical flow techniques
International Journal of Computer Vision
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Recovery of Ego-Motion Using Region Alignment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spline-Based Image Registration
International Journal of Computer Vision
Variational Optic Flow Computation with a Spatio-Temporal Smoothness Constraint
Journal of Mathematical Imaging and Vision
Optimal Control Formulation for Determining Optical Flow
SIAM Journal on Scientific Computing
Hierarchical Estimation and Segmentation of Dense Motion Fields
International Journal of Computer Vision
ECCV '90 Proceedings of the First European Conference on Computer Vision
Optical-Flow Estimation while Preserving Its Discontinuities: A Variational Approach
ACCV '95 Invited Session Papers from the Second Asian Conference on Computer Vision: Recent Developments in Computer Vision
CAIP '93 Proceedings of the 5th International Conference on Computer Analysis of Images and Patterns
Skin and Bones: Multi-layer, Locally Affine, Optical Flow and Regularization with Transparency
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Fast and Accurate Motion Estimation Using Orientation Tensors and Parametric Motion Models
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Dense 3D Interpretation of Image Sequences: A Variational Approach Using Anisotropic Diffusion
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Dense 3D Interpretation of Image Sequences: A Variational Approach Using Anisotropic Diffusion
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods
International Journal of Computer Vision
Motion Competition: A Variational Approach to Piecewise Parametric Motion Segmentation
International Journal of Computer Vision
On the Spatial Statistics of Optical Flow
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
IEEE Transactions on Pattern Analysis and Machine Intelligence
Highly Accurate Optic Flow Computation with Theoretically Justified Warping
International Journal of Computer Vision
A General Framework and New Alignment Criterion for Dense Optical Flow
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Joint optical flow estimation, segmentation, and 3D interpretation with level sets
Computer Vision and Image Understanding
Coarse to over-fine optical flow estimation
Pattern Recognition
Variational motion segmentation with level sets
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Revisiting the brightness constraint: probabilistic formulation and algorithms
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Accurate dense optical flow estimation using adaptive structure tensors and a parametric model
IEEE Transactions on Image Processing
A Variational Model for the Joint Recovery of the Fundamental Matrix and the Optical Flow
Proceedings of the 30th DAGM symposium on Pattern Recognition
An Unbiased Second-Order Prior for High-Accuracy Motion Estimation
Proceedings of the 30th DAGM symposium on Pattern Recognition
Is Dense Optic Flow Useful to Compute the Fundamental Matrix?
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Dynamic Texture Detection Based on Motion Analysis
International Journal of Computer Vision
Generalized least squares-based parametric motion estimation
Computer Vision and Image Understanding
Hyperbolic Numerics for Variational Approaches to Correspondence Problems
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
When Discrete Meets Differential
International Journal of Computer Vision
Optical Flow Computation from an Asynchronised Multiresolution Image Sequence
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
A compact harmonic code for early vision based on anisotropic frequency channels
Computer Vision and Image Understanding
Piecewise smooth affine registration of point-sets with application to DT-MRI brain fiber-data
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Variational optic flow on the Sony PlayStation 3
Journal of Real-Time Image Processing
Complex motion models for simple optical flow estimation
Proceedings of the 32nd DAGM conference on Pattern recognition
TriangleFlow: optical flow with triangulation-based higher-order likelihoods
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Can Variational Models for Correspondence Problems Benefit from Upwind Discretisations?
Journal of Mathematical Imaging and Vision
A Database and Evaluation Methodology for Optical Flow
International Journal of Computer Vision
Variational Multi-Valued Velocity Field Estimation for Transparent Sequences
Journal of Mathematical Imaging and Vision
A higher-order model for fluid motion estimation
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
Robust trajectory-space TV-L1 optical flow for non-rigid sequences
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Intermediate flow field filtering in energy based optic flow computations
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Dense versus Sparse Approaches for Estimating the Fundamental Matrix
International Journal of Computer Vision
Over-Parameterized optical flow using a stereoscopic constraint
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Robust optic-flow estimation with bayesian inference of model and hyper-parameters
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Articulated motion segmentation of point clouds by group-valued regularization
EG 3DOR'12 Proceedings of the 5th Eurographics conference on 3D Object Retrieval
Approximate regularization for structural optical flow estimation
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
Group-Valued regularization for analysis of articulated motion
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Adjustable linear models for optic flow based obstacle avoidance
Computer Vision and Image Understanding
Bundled camera paths for video stabilization
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
International Journal of Computer Vision
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A novel optical flow estimation process based on a spatio-temporal model with varying coefficients multiplying a set of basis functions at each pixel is introduced. Previous optical flow estimation methodologies did not use such an over parameterized representation of the flow field as the problem is ill-posed even without introducing any additional parameters: Neighborhood based methods of the Lucas---Kanade type determine the flow at each pixel by constraining the flow to be described by a few parameters in small neighborhoods. Modern variational methods represent the optic flow directly via the flow field components at each pixel. The benefit of over-parametrization becomes evident in the smoothness term, which instead of directly penalizing for changes in the optic flow, accumulates a cost of deviating from the assumed optic flow model. Our proposed method is very general and the classical variational optical flow techniques are special cases of it, when used in conjunction with constant basis functions. Experimental results with the novel flow estimation process yield significant improvements with respect to the best results published so far.