Computerized Flow Field Analysis: Oriented Texture Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct estimation and error analysis for oriented patterns
CVGIP: Image Understanding
Image models for 2-D flow visualization and compression
CVGIP: Graphical Models and Image Processing
Acquisition of Symbolic Description from Flow Fields: A New Approach Based on a Fluid Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Determination of singular points in 2D deformable flow fields
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
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
Extraction of Singular Points from Dense Motion Fields: An Analytic Approach
Journal of Mathematical Imaging and Vision
| Hi-index | 0.00 |
This paper is concerned with the analysis of 2D fluid motion from numerical images. The interpretation of such deformable flow fields can be derived from the characterization of linear motion models provided that first order approximations are considered in an adequate neighborhood of so-called singular points where the velocity becomes null. However, locating such points, delimiting this neighborhood, and estimating the associated 2D affine motion model, are intricate difficult problems. We explicitly address these three joint problems according to a statistical adaptive approach. In the fluid mechanics images we are dealing with, the motion model can be directly inferred from a single image, since the visualized form accounts for the underlying motion. We have developed an original method which relies on an orthogonality constraint between the spatial image gradient field and the motion model velocity field, while explicitly formalizing and handling both model and measurement noises. This method has been validated on several real fluid flow images.