IEEE Transactions on Pattern Analysis and Machine Intelligence
The existence of geometrical density—image transformations corresponding to object motion
Computer Vision, Graphics, and Image Processing
Field theory approach for determining optical flow
Pattern Recognition Letters
Image models for 2-D flow visualization and compression
CVGIP: Graphical Models and Image Processing
A scalar function formulation for optical flow
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
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
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Variational Optic Flow Computation with a Spatio-Temporal Smoothness Constraint
Journal of Mathematical Imaging and Vision
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Physically based fluid flow recovery from image sequences
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Extraction of Singular Points from Dense Motion Fields: An Analytic Approach
Journal of Mathematical Imaging and Vision
A Stochastic Filter for Fluid Motion Tracking
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Variational dense motion estimation using the Helmholtz decomposition
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
A novel parametric method for non-rigid image registration
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Vortex and source particles for fluid motion estimation
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
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
Dense estimation and object-based segmentation of the optical flow with robust techniques
IEEE Transactions on Image Processing
A Variational Technique for Time Consistent Tracking of Curves and Motion
Journal of Mathematical Imaging and Vision
Dynamic Texture Detection Based on Motion Analysis
International Journal of Computer Vision
A Geometric Framework and a New Criterion in Optical Flow Modeling
Journal of Mathematical Imaging and Vision
Robust processing of optical flow of fluids
IEEE Transactions on Image Processing
Complex motion models for simple optical flow estimation
Proceedings of the 32nd DAGM conference on Pattern recognition
International Journal of Computer Vision
A higher-order model for fluid motion estimation
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
Cardiac motion estimation using covariant derivatives and helmholtz decomposition
STACOM'11 Proceedings of the Second international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges
| Hi-index | 0.00 |
In this paper we propose a new motion estimator for image sequences depicting fluid flows. The proposed estimator is based on the Helmholtz decomposition of vector fields. This decomposition consists in representing the velocity field as a sum of a divergence free component and a vorticity free component. The objective is to provide a low-dimensional parametric representation of optical flows by depicting them as deformations generated by a reduced number of vortex and source particles. Both components are approximated using a discretization of the vorticity and divergence maps through regularized Dirac measures. The resulting so called irrotational and solenoidal fields consist of linear combinations of basis functions obtained through a convolution product of the Green kernel gradient and the vorticity map or the divergence map respectively. The coefficient values and the basis function parameters are obtained by minimization of a functional relying on an integrated version of mass conservation principle of fluid mechanics. Results are provided on synthetic examples and real world sequences.