Matching via Distance Profiles
YoonHaeng Hur Yuehaw Khoo
In this paper, we introduce and study matching methods based on distance
profiles. For the matching of point clouds, the proposed method is easily
implementable by solving a linear program, circumventing the computational
obstacles of quadratic matching. Also, we propose and analyze a flexible way to
execute location-to-location matching using distance profiles. Moreover, we
provide a statistical estimation error analysis in the context of
location-to-location matching using empirical process theory. Furthermore, we
apply our method to a certain model and show its noise stability by
characterizing conditions on the noise level for the matching to be successful.
Lastly, we demonstrate the performance of the proposed method and compare it
with some existing methods using synthetic and real data.