Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance of optical flow techniques
International Journal of Computer Vision
Digital video processing
Extracting 3D Vortices in Turbulent Fluid Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Tracking Nonrigid Motion and Structure from 2D Satellite Cloud Images without Correspondences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dense Estimation of Fluid Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
A simple fluid solver based on the FFT
Journal of Graphics Tools
Determining Optical Flow
Extraction of Singular Points from Dense Motion Fields: An Analytic Approach
Journal of Mathematical Imaging and Vision
Gerris: a tree-based adaptive solver for the incompressible Euler equations in complex geometries
Journal of Computational Physics
High-quality video view interpolation using a layered representation
ACM SIGGRAPH 2004 Papers
Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods
International Journal of Computer Vision
Vector-Valued Image Regularization with PDEs: A Common Framework for Different Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Low Dimensional Fluid Motion Estimator
International Journal of Computer Vision
Adaptive smoothing respecting feature directions
IEEE Transactions on Image Processing
Efficient and reliable schemes for nonlinear diffusion filtering
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Optical flow estimation and moving object segmentation based on median radial basis function network
IEEE Transactions on Image Processing
Prediction and tracking of moving objects in image sequences
IEEE Transactions on Image Processing
Stochastic differential equations and geometric flows
IEEE Transactions on Image Processing
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This paper proposes a new approach, coupling physical models and image estimation techniques, for modelling the movement of fluids. The fluid flow is characterized by turbulent movement and dynamically changing patterns which poses challenges to existing optical flow estimation methods. The proposed methodology, which relies on Navier-Stokes equations, is used for processing fluid optical flow by using a succession of stages such as advection, diffusion and mass conservation. A robust diffusion step jointly considering the local data geometry and its statistics is embedded in the proposed framework. The diffusion kernel is Gaussian with the covariance matrix defined by the local second derivatives. Such an anisotropic kernel is able to implicitly detect changes in the vector field orientation and to diffuse accordingly. A new approach is developed for detecting fluid flow structures such as vortices. The proposed methodology is applied on artificially generated vector fields as well as on various image sequences.