Particle Image Velocimetry by Feature Tracking

Digital Particle Image Velocimetry: a challenge for feature based tracking

Feature Correlation for Particle Image Velocimetry: An Application of Pattern Recognition

Particle Image Velocimetry (NV) has been used successfully for measuring instantaneous two dimensional velocity fields. Analyzing PIV images involves matching particle images capwred sequentially. In the usual practice, correlation (autoor cross-correlation) is used to find the displacement (hence velocity) of the particles within a large number of small "interrogation areas" in the field of vi...

Digital PIV : A Challenge for Feature Based Tracking 1 )

Motion tracking is an important step of the analysis of ow image sequences. However, Particle Image Velocimetry (PIV) techniques rarely use tracking methods developed in computer vision, they usually work with FFT and correlation based methods. Two major types of motion estimation algorithms exist: the optical ow and the feature based ones. Promising results have been obtained by applying optic...

Som Based Particle Matching for Volumetric Particle Tracking Velocimetry

Novel 3D image analysis and particle matching techniques for the use in the volumetric particle tracking velocimetry have been developed and tested by using synthetic images and experimental images of unsteady 3D flows. A tomography based particle reconstruction scheme along with the subsequent process of individual particle detection and validation was used. The detected particles in the two t...

An Efficient Target Tracking Algorithm Based on Particle Filter and Genetic Algorithm

In this paper, we propose an efficient hybrid Particle Filter (PF) algorithm for video tracking by employing a genetic algorithm to solve the sample impoverishment problem. In the presented method, the object to be tracked is selected by a rectangular window inside which a few numbers of particles are scattered. The particles’ weights are calculated based on the similarity between feature vecto...