Using optical flow equation for particle detection and velocity prediction in particle tracking

2012 ◽  
Vol 218 (17) ◽  
pp. 8684-8694 ◽  
Author(s):  
Luca Shindler ◽  
Monica Moroni ◽  
Antonio Cenedese
Keyword(s):  
Optical Flow ◽  
Particle Tracking ◽  
Flow Equation ◽  
Particle Detection ◽  
Velocity Prediction

Study on the existence of the transportation particle tracking model in the interactive medium

2021 ◽  
pp. 2150256
Author(s):  
Mohamed Abd Allah El-Hadidy ◽  
Alaa A. Alzulaibani
Keyword(s):  
Waiting Time ◽  
Particle Tracking ◽  
Necessary Conditions ◽  
Particle Detection ◽  
Particle Tracking Model ◽  
Search Plan ◽  
Tracking Process ◽  
Unit Speed ◽  
Tracking Model ◽  
Interactive Medium

This paper assumes that the particle jumps randomly (Guassian jumps) from one point to another along one of the imaginary lines inside the interactive medium. Since this study was done in the space, we consider that the position of the particle at any time [Formula: see text] has a multivariate distribution. The random waiting time of the particle for each Gaussian jump depends on its length. An identical set of programed nanosensors (with unit speed) were used to track this particle. Each line has a sensor that starts the tracking process from the origin. The existence of the necessary conditions which give the optimal search plan and the minimum expected value of the particle detection has been proven. This study is supported by a numerical example.

scholarly journals Cega: A Single Particle Segmentation Algorithm to Identify Moving Particles in a Noisy System

2020 ◽  
Author(s):  
Erin M. Masucci ◽  
Peter K. Relich ◽  
E. Michael Ostap ◽  
Erika L. F. Holzbaur ◽  
Melike Lakadamyali
Keyword(s):  
Particle Tracking ◽  
Single Particle ◽  
Molecular Motors ◽  
Single Particle Tracking ◽  
Particle Detection ◽  
Identification Rate ◽  
Detection Techniques ◽  
Noticeable Improvement

ABSTRACTImprovements to particle tracking algorithms are required to effectively analyze the motility of biological molecules in complex or noisy systems. A typical single particle tracking (SPT) algorithm detects particle coordinates for trajectory assembly. However, particle detection filters fail for datasets with low signal-to-noise levels. When tracking molecular motors in complex systems, standard techniques often fail to separate the fluorescent signatures of moving particles from background noise. We developed an approach to analyze the motility of kinesin motor proteins moving along the microtubule cytoskeleton of extracted neurons using the Kullback-Leibler (KL) divergence to identify regions where there are significant differences between models of moving particles and background signal. We tested our software on both simulated and experimental data and found a noticeable improvement in SPT capability and a higher identification rate of motors as compared to current methods. This algorithm, called Cega, for ‘find the object’, produces data amenable to conventional blob detection techniques that can then be used to obtain coordinates for downstream SPT processing. We anticipate that this algorithm will be useful for those interested in tracking moving particles in complex in vitro or in vivo environments.

scholarly journals Research on Moving Target Tracking Based on FDRIG Optical Flow

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1122
Author(s):  
Gong ◽  
Wang
Keyword(s):  
Optical Flow ◽  
Target Tracking ◽  
Research Work ◽  
Flow Equation ◽  
Superior Performance ◽  
Moving Target ◽  
Moving Target Tracking ◽  
Motion Sequence ◽  
Tracking Model ◽  
Vehicle Motion

Aiming at the problem of moving target recognition, a moving target tracking model based on FDRIG optical flow is proposed. First, the optical flow equation was analyzed from the theory of optical flow. Then, with the energy functional minimization, the FDRIG optical flow technique was proposed. Taking a road section of a university campus as an experimental section, 30 vehicle motion sequence images were considered as objects to form a vehicle motion sequence image with a complex background. The proposed FDRIG optical flow was used to calculate the vehicle motion optical flow field by the Halcon software. Comparable with the classic Horn and Schunck (HS) and Lucas and Kande (LK) optical flow algorithm, the monitoring results proved that the FDRIG optical flow was highly precise and fast when tracking a moving target. The Ettlinger Tor traffic scene was then taken as the second experimental object; FDRIG optical flow was used to analyze vehicle motion. The superior performance of the FDRIG optical flow was further verified. The whole research work shows that FDRIG optical flow has good performance and speed in tracking moving targets and can be used to monitor complex target motion information in real-time.

Research Object Rotational Motion Estimation Based on Digital Video Analysis Technique

2014 ◽  
Vol 635-637 ◽  
pp. 1001-1010
Author(s):  
Ji Hui Xu ◽  
Xiao Dong Zhu
Keyword(s):  
Motion Estimation ◽  
Optical Flow ◽  
Rotational Motion ◽  
Rigid Motion ◽  
Flow Equation ◽  
Motion Equations ◽  
Analysis Technique ◽  
Set Up ◽  
Simplified Calculation ◽  
Digital Video Analysis

This paper is about the object rotational motion estimation based on optical flow equation. Being a non-touch measuring technique, it is of important value in some special occasions. It is to set up rigid motion equations by optical flow character, and then using two-step iterative method to estimate motion equation and calculate rotation speed for each coordinate axis. We present a simplified calculation method for some object which specific structure parameters are known. The experiment results show the calculation is accurate.

Full-Field OCT Technique for High Speed Event-Based Optical Flow and Particle Tracking

2018 ◽  
Author(s):  
Xavier Berthelon ◽  
Guillaume Chenegros ◽  
Nicolas Libert ◽  
Jose-Alain Sahel ◽  
Kate Grieve ◽  
...  
Keyword(s):  
Optical Flow ◽  
Particle Tracking ◽  
High Speed ◽  
Full Field ◽  
Event Based

Fluid flow and optical flow

2008 ◽  
Vol 614 ◽  
pp. 253-291 ◽  
Author(s):  
TIANSHU LIU ◽  
LIXIN SHEN
Keyword(s):  
Fluid Flow ◽  
Optical Flow ◽  
Lagrange Equation ◽  
Fluid Velocity ◽  
Flow Equation ◽  
Flow Computation ◽  
Flow Method ◽  
Optical Flow Method ◽  
Optical Flow Computation ◽  
Particle Images

The connection between fluid flow and optical flow is explored in typical flow visualizations to provide a rational foundation for application of the optical flow method to image-based fluid velocity measurements. The projected-motion equations are derived, and the physics-based optical flow equation is given. In general, the optical flow is proportional to the path-averaged velocity of fluid or particles weighted with a relevant field quantity. The variational formulation and the corresponding Euler–Lagrange equation are given for optical flow computation. An error analysis for optical flow computation is provided, which is quantitatively examined by simulations on synthetic grid images. Direct comparisons between the optical flow method and the correlation-based method are made in simulations on synthetic particle images and experiments in a strongly excited turbulent jet.

Skin Friction Fields and Surface Dye Patterns on Delta Wings in Water Flows

2015 ◽  
Vol 137 (7) ◽  
Author(s):  
Tianshu Liu ◽  
M. H. M. Makhmalbaf ◽  
RS Vewen Ramasamy ◽  
S. Kode ◽  
P. Merati
Keyword(s):  
Optical Flow ◽  
Skin Friction ◽  
Delta Wing ◽  
Flow Equation ◽  
Delta Wings ◽  
Error Sources ◽  
Water Flows ◽  
Image Velocimetry ◽  
Intensity Images ◽  
The Relationship

This paper discusses the relationship between skin friction fields and surface dye patterns in surface luminescent dye visualizations in water flows, providing a theoretical foundation for extraction of high-resolution skin friction fields. The limiting form of the mass diffusion equation at a wall is recast as an optical flow equation connecting skin friction with the luminescent dye intensity. Snapshot solutions are obtained from a time sequence of luminescent intensity images by solving the optical flow equation via the variational method, and then a normalized skin friction field is reconstructed by averaging the snapshot solutions. An error analysis is given to identify the major error sources and the limitations of the technique. To evaluate the feasibility of this technique, surface luminescent dye visualizations on a 65 deg delta wing and a 76/40 deg double-delta wing are conducted in a water tunnel. The extracted skin friction topology on the delta wings and the velocity fields obtained by using particle image velocimetry (PIV) are discussed.

The Application of Positron Emission Particle Tracking (PEPT) to Study Inclusions in the Casting Process

2011 ◽  
Vol 690 ◽  
pp. 25-28 ◽  
Author(s):  
David J. Burnard ◽  
Joseph Gargiuli ◽  
Thomas Leadbeater ◽  
David J. Parker ◽  
William D. Griffiths
Keyword(s):  
Particle Tracking ◽  
Adsorption Process ◽  
Size Range ◽  
Surface Adsorption ◽  
Casting Process ◽  
Al Alloy ◽  
Particle Detection ◽  
Positron Emission ◽  
Oxide Particles ◽  
Exchange Surface

The behavior of inclusions in castings was studied using radioactive labeling of oxide particles located by Positron Emission Particle Tracking (PEPT). This uses the isotope18F, which has a half-life of 110 minutes, and allows particle detection within an accuracy of a few mm. Alumina and glass particles with a size range of 110 to 600 μm were made radioactive by an ion-exchange/surface adsorption process involving irradiated water. Individual radioactive particles were placed in resin-bonded sand moulds at known initial positions, and the moulds were filled with Al alloy, causing the particle to be entrained into the metal stream during the casting process. A modular γ-ray positron camera was used to track the paths of the particles within the mould, demonstrating the applicability of the technique to the study of inclusion behaviour in castings.

scholarly journals Computer Vision Aided Structural Identification: Feature Tracking Using Particle Tracking Velocimetry versus Optical Flow

Proceedings ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 33 ◽  
Author(s):  
Yunus Emre Harmanci ◽  
Zhilu Lai ◽  
Utku Gülan ◽  
Markus Holzner ◽  
Eleni Chatzi
Keyword(s):  
Computer Vision ◽  
Optical Flow ◽  
Particle Tracking ◽  
Particle Tracking Velocimetry ◽  
Large Scale ◽  
Video Recording ◽  
Structural Features ◽  
Spatial Density ◽  
Motion Magnification ◽  
Shear Frame

Recent advances in computer vision techniques allow to obtain information on the dynamic behaviour of structures using commercial grade video recording devices. The advantage of such schemes lies in the non-invasive nature of video recording and the ability to extract information at a high spatial density utilizing structural features. This creates an advantage over conventional contact sensors since constraints such as cabling and maximum channel availability are alleviated. In this study, two such schemes are explored, namely Particle Tracking Velocimetry (PTV) and the optical flow algorithm. Both are validated against conventional sensors for a lab-scale shear frame and compared. In cases of imperceptible motion, the recently proposed Phase-based Motion Magnification (PBMM) technique is employed to obtain modal information within frequency bands of interest and further used for modal analysis. The optical flow scheme combined with (PBMM) is further tested on a large-scale post-tensioned concrete beam and validated against conventional measurements, as a transition from lab- to outdoor field applications.

Sign in / Sign up

Export Citation Format

Share Document