Yücel, Harun; Akkus, B.; Cakirli Mutlu, R.B.; Gudekli, E.; Kinaci, B.; Guzelcimen, F.; Susoy Dogan, G.; Ozturk, F.C.; Ertoprak, A.

Digital video microscopy (DVM) is an important tool to obtain quantitative information about underlying physics in colloidal systems. DVM, however, has the overlap problem caused by particle's diffraction pattern when the detected particles are close to contact. This overlapping problem limits the tracking methods in determining accurate particle position that reflects all interactions exerting to particles or objects in colloidal studies. The problem occurs not only between two spherical particles close to each other but also when a spherical particle is close to a rod shaped particle which have a flat surface. In this study, the simulation results on the tracking spherical particle placed near the flat surface of a rod particle in 2D digital video microscopy are reported. This work shows that the overlap problem can be resolved by using a fraction of the intensity distribution of a spherical particle which is close to a flat surface.

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Yücel, Harun; Velu, Sabareesh K. P.; Oktem, Y.; Ozturk, F.C.; Dogan, G.S.; Guzelcimen, F.; Akkus, B.

Automated particle tracking algorithms are widely used by soft matter physicists as a research tool to detect and construct the trajectories of micron-sized particles in ffuids. Analyzing these trajectories will uncover the physics of the investigated particles mainly on the type of motion they undergo making them suitable for potential applications. A plethora of methods has been proposed and used for detection and tracking. In this work, we examine the performance of two commonly used tracking algorithms in terms of threshold dependencies in digital video images. One of them is the centroid method (CM), a well-known and used algorithm and the other is radial symmetry method (RSM) which is recently proposed. Here, we generate the synthetic digital video images consisting of randomly placed multiple particles and compare the absolute errors on the particle detection by varying threshold values. Our results suggest that both algorithms show dependence on the threshold value and on comparison RSM algorithm performs better than the CM algorithm when the noise level is zero. Moreover, the measured absolute errors show a strong dependence on threshold values when noise levels are increased (up to 20) especially for the RSM algorithm.

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