Abstract
Accurately measuring the translations of objects between images is essential
in many fields, including biology, medicine, chemistry, and physics. One
important application is tracking one or more particles by measuring their
apparent displacements in a series of images. Popular methods, such as the
center-of-mass, often require idealized scenarios to reach the shot-noise limit
of particle tracking and are, therefore, not generally applicable to multiple
image types. More general methods, like maximum likelihood estimation, reliably
approach the shot-noise limit, but are too computationally intense for use in
real-time applications. These limitations are significant, as real-time,
shot-noise-limited particle tracking is of paramount importance for feedback
control systems. To fill this gap, we introduce a new cross-correlation-based
algorithm that approaches shot-noise-limited displacement detection and a
GPU-based implementation for real-time image analysis of a single particle.