Performance of optical flow techniques
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reliable Estimation of Dense Optical Flow Fields with Large Displacements
International Journal of Computer Vision
Robot Vision
Computer Vision and Image Understanding - Special issue on empirical evaluation of computer vision algorithms
Numerical Recipes in C: The Art of Scientific Computing
Numerical Recipes in C: The Art of Scientific Computing
Hierarchical Estimation and Segmentation of Dense Motion Fields
International Journal of Computer Vision
Variational Methods for Multimodal Image Matching
International Journal of Computer Vision
Variational Stereovision and 3D Scene Flow Estimation with Statistical Similarity Measures
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods
International Journal of Computer Vision
Highly Accurate Optic Flow Computation with Theoretically Justified Warping
International Journal of Computer Vision
Piecewise-Smooth Dense Optical Flow via Level Sets
International Journal of Computer Vision
Coarse to over-fine optical flow estimation
Pattern Recognition
Robust motion estimation under varying illumination
Image and Vision Computing
Illumination-robust variational optical flow with photometric invariants
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
Improved reconstruction of deforming surfaces by cancelling ambient occlusion
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
GPU-based real-time spatio-temporal reconstruction studio
Proceedings of the 28th Spring Conference on Computer Graphics
On improving the robustness of variational optical flow against illumination changes
Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream
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We address the problem of variational optical flow for video processing applications that need fast operation and robustness to drastic variations in illumination. Recently, a solution [1] has been proposed based on the photometric invariants of the dichromatic reflection model [2]. However, this solution is only applicable to colour videos with brightness variations. Greyscale videos, or colour videos with colour illumination changes cannot be adequately handled. We propose two illumination-robust variational methods based on cross-correlation that are applicable to colour and grey-level sequences and robust to brightness and colour illumination changes. First, we present a general implicit nonlinear scheme that assumes no particular analytical form of energy functional and can accommodate different image components and data metrics, including cross-correlation. We test the nonlinear scheme on standard synthetic data with artificial brightness and colour effects added and conclude that cross-correlation is robust to both kinds of illumination changes. Then we derive a fast linearised numerical scheme for cross-correlation based variational optical flow. We test the linearised algorithm on challenging data and compare it to a number of state-of-the-art variational flow methods.