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弹性曲线上地标点的自动检测与不确定性量化

Automatic Detection and Uncertainty Quantification of Landmarks on Elastic Curves.

作者信息

Strait Justin, Chkrebtii Oksana, Kurtek Sebastian

机构信息

Department of Statistics, University of Georgia.

Department of Statistics, The Ohio State University.

出版信息

J Am Stat Assoc. 2019;114(527):1002-1017. doi: 10.1080/01621459.2018.1527224. Epub 2019 Mar 20.

DOI:10.1080/01621459.2018.1527224
PMID:31595098
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6781625/
Abstract

A population quantity of interest in statistical shape analysis is the location of landmarks, which are points that aid in reconstructing and representing shapes of objects. We provide an automated, model-based approach to inferring landmarks given a sample of shape data. The model is formulated based on a linear reconstruction of the shape, passing through the specified points, and a Bayesian inferential approach is described for estimating unknown landmark locations. The question of how many landmarks to select is addressed in two different ways: (1) by defining a criterion-based approach, and (2) joint estimation of the number of landmarks along with their locations. Efficient methods for posterior sampling are also discussed. We motivate our approach using several simulated examples, as well as data obtained from applications in computer vision, biology and medical imaging.

摘要

统计形状分析中一个重要的总体数量是地标点的位置,地标点是有助于重建和表示物体形状的点。我们提供了一种基于模型的自动化方法,用于在给定形状数据样本的情况下推断地标点。该模型基于形状的线性重建来制定,通过指定的点,并描述了一种贝叶斯推理方法来估计未知的地标点位置。关于选择多少地标点的问题以两种不同方式解决:(1)通过定义一种基于准则的方法,以及(2)联合估计地标点的数量及其位置。还讨论了后验采样的有效方法。我们通过几个模拟示例以及从计算机视觉、生物学和医学成像应用中获得的数据来推动我们的方法。

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