Particle tracking is a widespread research question for quantitative biology. In contrast to other approaches, we developed a local greedy technique based on the Kalman filter. To overcome the problem of guessing the first state of a particle, the algorithm runs iteratively in forward and backward direction. The algorithm was successfully tested with simulated and real data.
CITATION STYLE
Bürger, B., & Hesser, J. (2009). Concurrent particle tracking using an iterative Kalman filter approach. In Informatik aktuell (pp. 430–433). Kluwer Academic Publishers. https://doi.org/10.1007/978-3-540-93860-6_87
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