2D point set registration via stochastic particle flow filter

Stephen R. Porter

doi:10.1117/12.2585309

12 April 2021 2D point set registration via stochastic particle flow filter

Stephen R. Porter

Proceedings Volume 11756, Signal Processing, Sensor/Information Fusion, and Target Recognition XXX; 117560H (2021) https://doi.org/10.1117/12.2585309
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

This paper presents an innovative image registration algorithm using the particle flow filter. The particle flow filter is an efficient Bayesian filter that uses particles to represent probability densities. It is not constrained to the highly restrictive unimodal, linear, and Gaussian assumptions of many other Bayesian filters. The particle flow filter algorithms were implemented in MATLAB for 2D rigid body point-set registration. Additionally, the particle filter method and iterative closest point algorithms were implemented for comparison. For the same alignment accuracy, the new particle flow filter approach was 169% faster than the particle filter for certain challenging problems. For the same alignment time, the particle flow filter reduced misalignment by as much as 25% over that of the particle filter. The particle flow filter achieved 100% alignment with enough particles, and reduced misalignment by as much as 75% over that of iterative closest point. These results demonstrate that image registration via the particle flow filter significantly outperforms the particle filter and iterative closest point algorithms in the presence of noise and a high degree of initial misalignment.

Conference Presentation

Citation Download Citation

Stephen R. Porter "2D point set registration via stochastic particle flow filter", Proc. SPIE 11756, Signal Processing, Sensor/Information Fusion, and Target Recognition XXX, 117560H (12 April 2021); https://doi.org/10.1117/12.2585309

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