Gaussian process post-processing for particle tracking velocimetry

Real-time image processing for particle tracking velocimetry

A variational approach for particle tracking velocimetry

We introduce a novel variational approach for evaluating PTV image pairs and sequences in two and three dimensions. We combine a discrete non-differentiable particle matching term with a continuous regularization term. An advanced mathematical method guarantees convergence to a local minimum. The experimental evaluation shows that our variational method competes with three alternative approache...

Using Gaussian Process Annealing Particle Filter for 3D Human Tracking

We present an approach for human body parts tracking in 3D with prelearned motion models using multiple cameras. Gaussian process annealing particle filter is proposed for tracking in order to reduce the dimensionality of the problem and to increase the tracker’s stability and robustness. Comparing with a regular annealed particle filter-based tracker, we show that our algorithm can track bette...

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...

Applying Feature Tracking to Particle Image Velocimetry

Particle Image Velocimetry (PIV) is a popular approach to flow visualisation and measurement in hydroand aerodynamic studies and applications [7]. The fluid is seeded with particles that follow the flow and make it visible. Traditionally, correlation techniques have been used to estimate the displacements of the particles in a digital PIV sequence. Recently, we have successfully applied to PIV ...