An efficient computational algorithm for Hausdorff distance based on points-ruling-out and systematic random sampling
作者：
Highlights：
• A novel algorithm for fast and accurate Hausdorff distance computation is proposed.
• The strategies of systematic random sampling and points-ruling-out are utilized.
• 3-D point cloud models and real brain tumor volumes are used for test sets.
• Good performance under large-scale, highly overlapping point sets and different grid sizes.
• The proposed method outperforms state-of-the-art methods in performance.
摘要
•A novel algorithm for fast and accurate Hausdorff distance computation is proposed.•The strategies of systematic random sampling and points-ruling-out are utilized.•3-D point cloud models and real brain tumor volumes are used for test sets.•Good performance under large-scale, highly overlapping point sets and different grid sizes.•The proposed method outperforms state-of-the-art methods in performance.
论文关键词：Hausdorff distance,Computational complexity,Point matching,3-D point sets
论文评审过程：Received 26 November 2019, Revised 31 August 2020, Accepted 17 January 2021, Available online 2 February 2021, Version of Record 16 February 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.107857
