IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Analysis Using Multigrid Relaxation Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multidimensional Orientation Estimation with Applications to Texture Analysis and Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Computing optical flow via variational techniques
SIAM Journal on Applied Mathematics
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
Optimal Control Formulation for Determining Optical Flow
SIAM Journal on Scientific Computing
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
Combining the Advantages of Local and Global Optic Flow Methods
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Generalized image matching by the method of differences
Generalized image matching by the method of differences
A Multigrid Approach for Hierarchical Motion Estimation
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A Multigrid Approach for Hierarchical Motion Estimation
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
Stability and Uniqueness for the Crack Identification Problem
SIAM Journal on Control and Optimization
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
A Statistical Confidence Measure for Optical Flows
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
An adaptive confidence measure for optical flows based on linear subspace projections
Proceedings of the 29th DAGM conference on Pattern recognition
Optical flow: a curve evolution approach
IEEE Transactions on Image Processing
Variational optical flow computation in real time
IEEE Transactions on Image Processing
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We consider a variational model for the determination of the optic-flow in a general setting of non-smooth domains. This problem is ill-posed and its solution with PDE techniques includes a regularization procedure. The goal of this paper is to study a method to solve the optic flow problem and to control the effects of the regularization by allowing, locally and adaptively the choice of its parameters. The regularization in our approach is not controlled by a single parameter but by a function of the space variable. This results in a dynamical selection of the variational model which evolves with the variations of this function. Such method consists of new adaptive finite element discretization and an a posteriori strategy for the control of the regularization in order to achieve a trade-off between the data and the smoothness terms in the energy functional. We perform the convergence analysis and the a posteriori analysis, and we prove that the error indicators provide, as, a by-product, a confidence measure which shows the effects of regularization and serves to compute sparse solutions. We perform several numerical experiments, to show the efficiency and the reliability of the method in the computations of optic flow, with high accuracy and of low density.