Landmark-based algorithms for group average and pattern recognition
作者：
Highlights：
• A robust landmark-based algorithm for finding the geometric median (group average) of a group of shapes with heavy outliers is proposed.
• The underlying Log and Exp maps of the proposed algorithm belong to a class of efficient geodesic shooting algorithms for template matching.
• Once the group average is found, the Hamiltonian metric computed between the group average and each member of the group can be used for clustering analysis.
• The proposed algorithms, as a tool of feature extraction, extract information about momentum at each landmark during the process of finding the geometric median.
• The local momenta found by the proposed algorithm are useful for some classification purposes that cannot be achieved by using the landmark locations.
摘要
•A robust landmark-based algorithm for finding the geometric median (group average) of a group of shapes with heavy outliers is proposed.•The underlying Log and Exp maps of the proposed algorithm belong to a class of efficient geodesic shooting algorithms for template matching.•Once the group average is found, the Hamiltonian metric computed between the group average and each member of the group can be used for clustering analysis.•The proposed algorithms, as a tool of feature extraction, extract information about momentum at each landmark during the process of finding the geometric median.•The local momenta found by the proposed algorithm are useful for some classification purposes that cannot be achieved by using the landmark locations.
论文关键词：Group average,Pattern recognition,Features extraction,Landmark,Template matching,Residual momentum,Cluster analysis,Outliers,Structure abnormality
论文评审过程：Received 21 February 2018, Revised 15 August 2018, Accepted 5 September 2018, Available online 13 September 2018, Version of Record 22 September 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.09.002
