2026-10-30T05:18:53Zhttps://bayburt-rdlopen.rawdatalibrary.net/oai/request

oai:bayburt-rdlopen.rawdatalibrary.net:bayburt/120982026-10-29T19:42:14Zopenairedrivercom_bayburt_10000col_bayburt_10001com_bayburt_10010col_bayburt_10222

Toolbox for tracking and analyzing crowded mixture of colloidal particlesYücel, HarunVelu, Sabareesh K.P.Binary colloidsCrowded particlesDigital video microscopy (DVM)Image processingParticle classificationParticle detectionParticle trackingWe present a MATLAB-based multiple particle tracking (mPosTracker) algorithm to detect and track crowded colloidal particles that are two-dimensionally imaged using digital video microscopy. Our algorithm adopts three optional detection methods using threshold settings namely; centroid method (CM), radial symmetry method (RSM) and partial radial symmetry method (pRSM). In contrast to other reported algorithms, it allows classifying a maximum of three types of particles based on area or intensity. Furthermore, we include a toolbox to perform quantitative particle analysis like number density, trajectories with mean square displacement, and Voronoi cells with six-fold bond parameters to reveal the physics of examined dataset. To demonstrate the abilities of mPosTracker, we show the analysis outcomes on synthetically and experimentally acquired time-lapse microscopy datasets. mPosTracker is suitable for studying the dynamics of crowd behaviour, self-assembly, and manipulation of multi-component colloids under external fields.Elsevier B.V.2021Articlehttps://rdlopen.rawdatalibrary.net/r/bayburt/12098https://doi.org/10.1016/j.colcom.2021.1005462215-0382enColloids and Interface Science Communicationsinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://bayburt-rdlopen.rawdatalibrary.net/tr/items/bayburt/12098