Two-frame 3D particle tracking

By H. Stüer, E. Graff, Morteza Gharib. 2006

211 citations recorded by the source corpus.

Subfields: Biophysics, Computational Mechanics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/9434b84bbde7dd301ae3c33d28ebc2bea6821cd1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

1. Christian Cierpka
2. Bernhard Wieneke
3. Christian J. Kähler
4. B. Lütke

In Computational Mechanics

1. Christian Cierpka
2. Christian J. Kähler
3. Bernhard Wieneke
4. B. Lütke
5. R. Hain
6. Rodrigo Segura
7. Fulvio Scarano
8. B. W. van Oudheusden

In Statistical and Nonlinear Physics

1. Christian Cierpka
2. Bernhard Wieneke
3. Christian J. Kähler
4. B. Lütke

Citations in the corpus

• A simple single camera 3C3D velocity measurement technique without errors due to depth of correlation and spatial averaging for microfluidics (2010, background): “[25], and to measure trajectories and acceleration of the particles.”
• Iterative reconstruction of volumetric particle distribution (2013, methodology): “Tracking individual particles in a volume over time (3D-PTV; 3D-particle tracking velocimetry, also called volumetric PTV or DDPIV, Pereira et al 2006) has been employed for 3D fluid motion analysis for many decades (Maas et al 1993).”
• Higher order multi-frame particle tracking velocimetry (2013, methodology): “Since the approach only works for very low particle image densities, artificial neural networks or relaxation methods can be used to minimize a local or global cost function (Pereira et al. 2006) to allow for higher seeding concentrations.”