PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Robust Processing of Optical Flow of Fluids
A. Doshi and Adrian Bors
IEEE TRANSACTIONS ON IMAGE PROCESSING Volume 19, Number 9, pp. 2332-2344, 2010. ISSN 1057-7149

Abstract

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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:8438
Deposited By:Adrian Bors
Deposited On:06 January 2012