GPU acceleration of robust point matching

Authors:
Chad Mourning;Scott Nykl;Huihui Xu;David Chelberg;Jundong Liu
Affiliations:
School of Electrical Engineering and Computer Science, Ohio University, Athens, Ohio;School of Electrical Engineering and Computer Science, Ohio University, Athens, Ohio;School of Electrical Engineering and Computer Science, Ohio University, Athens, Ohio;School of Electrical Engineering and Computer Science, Ohio University, Athens, Ohio;School of Electrical Engineering and Computer Science, Ohio University, Athens, Ohio
Venue:
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Year:
2010

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Abstract

Robust Point Matching (RPM) is a common image registration algorithm, yet its large computational complexity prohibits registering large point sets in a timely manner. With recent advances in General Purpose Graphical Processing Units (GPGPUs), commodity hardware is capable of greatly reducing the execution time of RPM when non-rigidly aligning thousands of data points. In this paper, we identify areas where parallelism can be exploited in the RPM algorithm, and investigate a GPU-based approach to accelerate the implementation. Other common RPM implementations are compared with our solution. Experiments on synthetic and real data sets show that our approach achieves close to linear speed-up with respect to total computational power over the widely used Matlab implementation. Our tests indicate that utilizing our implementation on current state of the art GPU technology would enable the use of vastly greater point set sizes.