Optical Flow Estimation: An Error Analysis of Gradient-Based Methods with Local Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computation of component image velocity from local phase information
International Journal of Computer Vision
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
Variational Optic Flow Computation with a Spatio-Temporal Smoothness Constraint
Journal of Mathematical Imaging and Vision
ECCV '90 Proceedings of the First European Conference on Computer Vision
Spatiotemporally Adaptive Estimation and Segmenation of OF-Fields
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
A Variational Approach to the Design of Early Vision Algorithms
Proceedings of the 7th TFCV on Theoretical Foundations of Computer Vision
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
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
Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods
International Journal of Computer Vision
Motion and Appearance Nonparametric Joint Entropy for Video Segmentation
International Journal of Computer Vision
Building Blocks for Computer Vision with Stochastic Partial Differential Equations
International Journal of Computer Vision
Optimal filters for extended optical flow
IWCM'04 Proceedings of the 1st international conference on Complex motion
Towards a multi-camera generalization of brightness constancy
IWCM'04 Proceedings of the 1st international conference on Complex motion
Incorporating social entropy for crowd behavior detection using SVM
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Control of the Effects of Regularization on Variational Optic Flow Computations
Journal of Mathematical Imaging and Vision
II-LK – a real-time implementation for sparse optical flow
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
Optical flow estimation in cardiac CT images using the steered Hermite transform
Image Communication
Computers in Biology and Medicine
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Differential methods are frequently used techniques for optic flow computations. They can be classified into local methods such as the Lucas-Kanade technique or Big眉n's structure tensor method, and into global methods such as the Horn-Schunck approach and its modifications. Local methods are known to be more robust under noise, while global techniques yield 100% dense flow fields. No clear attempts to combine the advantages of these two classes of methods have been made in the literature so far.This problem is addressed in our paper. First we juxtapose the role of smoothing processes that are required in local and global differential methods for optic flow computation. This discussion motivates us to introduce and evaluate a novel method that combines the advantages of local and global approaches: It yields dense flow fields that are robust against noise. Finally experiments with different sequences are performed demonstrating its excellent results.