Performance of optical flow techniques
International Journal of Computer Vision
The computation of optical flow
ACM Computing Surveys (CSUR)
Computing optical flow via variational techniques
SIAM Journal on Applied Mathematics
Reliable Estimation of Dense Optical Flow Fields with Large Displacements
International Journal of Computer Vision
Dense Estimation of Fluid Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theoretical Framework for Convex Regularizers in PDE-Based Computation of Image Motion
International Journal of Computer Vision
Extraction of Singular Points from Dense Motion Fields: An Analytic Approach
Journal of Mathematical Imaging and Vision
International Journal of Computer Vision
Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations (Applied Mathematical Sciences)
Symmetrical Dense Optical Flow Estimation with Occlusions Detection
International Journal of Computer Vision
A variational approach for 3D motion estimation of incompressible PIV flows
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
3D motion estimation using a combination of correlation and variational methods for PIV
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
Discrete orthogonal decomposition and variational fluid flow estimation
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
Domain decomposition for variational optical-flow computation
IEEE Transactions on Image Processing
3D Video Based Segmentation and Motion Estimation with Active Surface Evolution
Journal of Signal Processing Systems
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Motion estimation has many applications in fluid analysis, and a lot of work has been carried out using Particle Image Velocimetry (PIV) to capture and measure the flow motion from sequences of 2D images. Recent technological advances allow capturing 3D PIV sequences of moving particles. In the context of 3D flow motion, the assumption of incompressibility is an important physical property that is satisfied by a large class of problems and experiments. Standard motion estimation techniques in computer vision do not take into account the physical constraints of the flow, which is a very interesting and challenging problem. In this paper, we propose a new variational motion estimation technique which includes the incompressibility of the flow as a constraint to the minimization problem. We analyze, from a theoretical point of view, the influence of this constraint and we design a new numerical algorithm for motion estimation which enforces it. The performance of the proposed technique is evaluated from numerical experiments on synthetic and real data.