Landmark-based algorithms for group average and pattern recognition

PATTERN RECOGNITION

卷:86

Article

Huzurbazar, Snehalata¹  Kuang, Dongyang²  Lee, Long³ 

[1] West Virginia Univ, Dept Biostat, Sch Publ Hlth, 1 Med Ctr Dr,POB 9190, Morgantown, WV 26506 USA

[2] Univ Ottawa, Dept Math & Stat, Ottawa, ON, Canada

[3] Univ Wyoming, Dept Math & Stat, Math, Laramie, WY 82071 USA

关键词: Group average;    Pattern recognition;    Features extraction;    Landmark;    Template matching;    Residual momentum;    Cluster analysis;    Outliers;    Structure abnormality;   

DOI  :  10.1016/j.patcog.2018.09.002

来源: Elsevier

【 摘 要 】

We introduce a class of mathematical algorithms with the aim of establishing a framework of finding a group average and extracting prominent features in a group of landmark represented shapes or image templates. A group average is an estimator that is said to best represent the common features of the group being studied. The proposed algorithms, as a tool of feature extraction, extract information about momentum at each landmark through the process of template matching. Once the convergence criterion is satisfied numerically, the algorithms produce a group average and a local coordinate system for each member of the observing group, in terms of the residual momentum. We present several examples to illustrate the use of the proposed algorithms for finding a group average. Using the metrics computed between the group average and each member of the group, we successfully run a cluster analysis for datasets that contain a heavy percentage of outliers. Finally, we apply the collected residual momenta computed in the proposed algorithms in some statistical methods to demonstrate a potential application of the algorithms for detecting structure abnormality. (C) 2018 Elsevier Ltd. All rights reserved.

【 授权许可】

Free