What is it about?
We unify Discrete Frechet Distance and Dynamic Time-Warping, in order to measure similarity of polygonal lines, or discrete curves. We propose improved algorithms for similarity computation. Moreover, our novel methods offer a trade-off between time and space complexity.
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Why is it important?
A famous concrete example is to minimize the leash when walking your dog. Discrete curves are important in time-series analytics, comparison of trajectories in 2D and 3D, as well as computing the distance of one-dimensional structures such as molecular backbones.
Perspectives
Our algorithms are easy enough to get implemented and we are working on such software. In practice, of course, further simplifications are possible because asymptotic complexity bounds are usually pessimistic.
Ioannis Emiris
National and Kapodistrian University of Athens
Read the Original
This page is a summary of: Products of Euclidean Metrics, Applied to Proximity Problems among Curves, ACM Transactions on Spatial Algorithms and Systems, August 2020, ACM (Association for Computing Machinery),
DOI: 10.1145/3397518.
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