The existence of geometrical density—image transformations corresponding to object motion
Computer Vision, Graphics, and Image Processing
Distance measures for signal processing and pattern recognition
Signal 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
Image Sequence Analysis via Partial Differential Equations
Journal of Mathematical Imaging and Vision
Dense Estimation of Fluid Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Estimation and Segmentation of Dense Motion Fields
International Journal of Computer Vision
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
Dense estimation and object-based segmentation of the optical flow with robust techniques
IEEE Transactions on Image Processing
Automatic tropical cyclone eye fix using genetic algorithm
Expert Systems with Applications: An International Journal
Variational dense motion estimation using the Helmholtz decomposition
Scale Space'03 Proceedings of the 4th international conference on Scale space 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
Vortex tracking in high density vector fields
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
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Analyzing fluid motion is essential in number of domains and can rarely be handled using generic computer vision techniques. In this particular application context, we address two distinct problems. First we describe a dedicated dense motion estimator. The approach relies on constraints issuing from fluid motion properties and allows us to recover dense motion fields of good quality. Secondly, we address the problem of analyzing such velocity fields. We present a kind of motion-based segmentation relying on an analytic representation of the motion field that permits to extract important quantities such as singularities, stream-functions or velocity potentials. The proposed method has the advantage to be robust, simple, and fast.