# Particle Detection Algorithms for Complex Plasmas

## TL;DR

This paper evaluates various particle detection algorithms in complex plasmas, combining image processing techniques to improve accuracy, and provides insights applicable to other particle tracking fields like colloids and granular matter.

## Contribution

It systematically compares algorithms and processing methods, including automatic threshold detection, to enhance particle position accuracy in complex plasma imaging.

## Key findings

- Different algorithms have unique advantages depending on the scenario.
- Automatic threshold detection improves robustness of particle identification.
- Results are applicable to other fields involving image-based particle tracking.

## Abstract

In complex plasmas, the behavior of freely floating micrometer sized particles is studied. The particles can be directly visualized and recorded by digital video cameras. To analyze the dynamics of single particles, reliable algorithms are required to accurately determine their positions to sub-pixel accuracy from the recorded images. Typically, straightforward algorithms are used for this task. Here, we combine the algorithms with common techniques for image processing. We study several algorithms and pre- and post-processing methods, and we investigate the impact of the choice of threshold parameters, including an automatic threshold detection. The results quantitatively show that each algorithm and method has its own advantage, often depending on the problem at hand. This knowledge is applicable not only to complex plasmas, but useful for any kind of comparable image-based particle tracking, e.g. in the field of colloids or granular matter.