Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Reliable Estimation of Dense Optical Flow Fields with Large Displacements
International Journal of Computer Vision
Variational Optic Flow Computation with a Spatio-Temporal Smoothness Constraint
Journal of Mathematical Imaging and Vision
Multi-Frame Correspondence Estimation Using Subspace Constraints
International Journal of Computer Vision
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods
International Journal of Computer Vision
Nonrigid Structure-from-Motion: Estimating Shape and Motion with Hierarchical Priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Variational Approach to Adaptive Correlation for Motion Estimation in Particle Image Velocimetry
Proceedings of the 30th DAGM symposium on 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
Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient and reliable schemes for nonlinear diffusion filtering
IEEE Transactions on Image Processing
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
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In this paper we describe a variational approach to computing dense optic flow in the case of non-rigid motion. We optimise a global energy to compute the optic flow between each image in a sequence and a reference frame simultaneously. Our approach is based on subspace constraints which allow to express the optic flow at each pixel in a compact way as a linear combination of a 2D motion basis that can be pre-estimated from a set of reliable 2D tracks. We reformulate the multi-frame optic flow problem as the estimation of the coefficients that multiplied with the known basis will give the displacement vectors for each pixel. We adopt a variational framework in which we optimise a nonlinearised global brightness constancy to cope with large displacements and impose homogeneous regularization on the multi-frame motion basis coefficients. Our approach has two strengths. First, the dramatic reduction in the number of variables to be computed (typically one order of magnitude) which has obvious computational advantages and second, the ability to deal with large displacements due to strong deformations. We conduct experiments on various sequences of non-rigid objects which show that our approach provides results comparable to state of the art variational multi-frame optic flow methods.