Neural network approach to stereoscopic correspondence of three-dimensional particle tracking velocimetry

Abstract

Particle tracking velocimetry (PTV) is a reliable measurement technique for the quantitative study of fluid flows by observing the motion of the particles seeded in them and is widely used in several industrial applications. The nature of the flow can be precisely observed only if all the three components of the velocity are computed. In 3-D PTV system, particles viewed by two (or more than two) stereoscopic cameras with a parallax have to be correctly paired at every synchronized time step. This is important because the 3-D coordinates of individual particles cannot be computed without the knowledge of the correct stereo correspondence of the particles. In the present work, a neural network-based algorithm has been proposed for the stereoscopic particle pairing process. The correspondence between the particle pairs is modeled as a constrained optimization problem. The constraints are provided on the basis of the epipolar geometry of the particle images and on the basis of the uniqueness of the matched pairs. The results are tested with various standard images. © 2008 Institute of Electrical Engineers of Japan.