Abstract
Particle and object tracking is gaining attention in industrial applications and is commonly applied in: colloidal, biophysical, ecological, and micro-fluidic research. Reliable tracking information is heavily dependent on the system under study and algorithms that correctly determine particle position between images. However, in a real environmental context with the presence of noise including particular or dissolved matter in water, and low and fluctuating light conditions, many algorithms fail to obtain reliable information. We propose a new algorithm, the Circular Symmetry algorithm (C-Sym), for detecting the position of a circular particle with high accuracy and precision in noisy conditions. The algorithm takes advantage of the spatial symmetry of the particle allowing for subpixel accuracy. We compare the proposed algorithm with four different methods using both synthetic and experimental datasets. The results show that C-Sym is the most accurate and precise algorithm when tracking micro-particles in all tested conditions and it has the potential for use in applications including tracking biota in their environment.
Highlights
Tracking micro-particles with computer-enhanced video microscopy is a common technique in biophysics, micro-fluidics and colloidal research [1,2,3]
Particle position refinement algorithm not used to build our Circular Symmetry algorithm (C-Sym) algorithm but instead used to compare C-Sym with five commonly used algorithms: Circular Hough Transform (CHT), CoM, XCorr, Quadrant Interpolation (QI) and Gaussian fitting (GFit), details of these latter algorithms are described in the Supporting Information S1 File
We propose a new algorithm, denoted the Circular Symmetry algorithm (C-Sym), for accurately locating the center of a circular particle
Summary
Tracking micro-particles with computer-enhanced video microscopy is a common technique in biophysics, micro-fluidics and colloidal research [1,2,3]. Tracking micro-particles is common when designing micro-fluidic devices [10]. To obtain reliable data of motion, it is important that particle positions are accurately determined [11]. Accuracy is limited by the detection algorithm [12] and the microscope spatial resolution, which is determined by the quality of the optics and the wavelength of the light. To improve accuracy several algorithms have been developed during the years: e.g., Center-ofMass (CoM) [13], Gaussian fitting (GFit) [14,15], Cross-Correlation (XCorr) [16,17], quadrant
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