Work

Model Segmentation in Single Particle Tracking

Year: 2021

Type: article

Abstract: In this paper, we implement and compare two different change detection techniques applied to determining the time points in Single Particle Tracking (SPT) data where the particle changes the dynamic model of motion. The goal is to use this change detection to segment the data in order to estimate the relevant parameters of such models. We consider two well-known statistics commonly used for change detection: the likelihood ratio test (LRT) and the Kullback-Leibler divergence (KLD). We assume that our time-varying system is subject to step-like changes in the parameters that drive the process. The techniques are then applied to experimental data acquired on a microscope under controlled settings to validate our results.

Source: IFAC-PapersOnLine

DOI: 10.1016/j.ifacol.2021.11.197

Authors: Boris I. Godoy, Nicholas A. Vickers, Sean B. Andersson

Institution: Boston University

Language: English

Topic: Target Tracking and Data Fusion in Sensor Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: National Institute of General Medical Sciences

Award: Joint estimation of motion model, model parameters, and particle trajectories in single particle tracking