Gauges and gauge transformations for uncertainty description of geometric structure with indeterminacy

被引:24
作者
Kanatani, K
Morris, DD
机构
[1] Gunma Univ, Dept Comp Sci, Gunma, Japan
[2] Carnegie Mellon Univ, Inst Robot, Pittsburgh, PA 15213 USA
关键词
computer vision; Cramer-Rao lower bound; gauge transformation; geometric indeterminacy; Lie group theory; statistical estimation; uncertainty description;
D O I
10.1109/18.930934
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper presents a consistent theory for describing indeterminacy and uncertainty of three-dimensional (3-D) reconstruction from a sequence of images. First, we give a group-theoretical analysis of gauges and gauge transformations. We then discuss how to evaluate the reliability of the solution that has indeterminacy and extend the Cramer-Rao lower bound to incorporate internal indeterminacy, We also introduce the free-gauge approach and define the normal form of a covariance matrix that is independent of particular gauges, Finally, we show simulated and real-image examples to illustrate the effect of gauge freedom on uncertainty description.
引用
收藏
页码:2017 / 2028
页数:12
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