Particle image models for optical flow-based velocity field estimation in image velocimetry

Grzegorz Głomb; Grzegorz Świrniak

doi:10.1117/12.2306630

24 May 2018 Particle image models for optical flow-based velocity field estimation in image velocimetry

Grzegorz Głomb, Grzegorz Świrniak

Proceedings Volume 10679, Optics, Photonics, and Digital Technologies for Imaging Applications V; 106791K (2018) https://doi.org/10.1117/12.2306630
Event: SPIE Photonics Europe, 2018, Strasbourg, France

Abstract

This paper examines two models for image representation used for optical flow estimation in Particle Image Velocimetry (PIV). The common approach for flow estimation bases on a cross-correlation between PIV images. An alternative solution bases on an optical flow, which has the advantage of calculating vector fields with much better spatial resolution. The optical flow-based estimation requires calculations of temporal and spatial derivatives of the image intensity, which is usually achieved by using finite differences. Due to rapid intensity changes in the PIV images caused by particles having small diameters, an exact estimation of spatial derivatives using finite differences may lead to numerical errors that render data interpretation limited or even impractical. The present study aims at solving this problem by introducing two algorithms for PIV image processing, which differs in terms of a digital image representation. Both algorithms rely on a PIV image model, wherein the particle image complies with an Airy disc, which is well approximated by using a Gaussian function. Numerical analysis of sample PIV images (uniform and turbulent fields) show that both methods allow for high precision flow-velocity fields estimates in conjunction with the Lucas-Kanade algorithm.

Citation Download Citation

Grzegorz Głomb and Grzegorz Świrniak "Particle image models for optical flow-based velocity field estimation in image velocimetry", Proc. SPIE 10679, Optics, Photonics, and Digital Technologies for Imaging Applications V, 106791K (24 May 2018); https://doi.org/10.1117/12.2306630

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available