Uncertainty modeling and model selection for geometric inference

被引:42
作者
Kanatani, K [1 ]
机构
[1] Okayama Univ, Dept Informat Technol, Okayama 7008530, Japan
关键词
statistical method; feature point extraction; asymptotic evaluation; geometric AIC; geometric MDL;
D O I
10.1109/TPAMI.2004.93
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We first investigate the meaning of "statistical methods" for geometric inference based on image feature points. Tracing back the origin of feature uncertainty to image processing operations, we discuss the implications of asymptotic analysis in reference to "geometric fitting" and "geometric model selection" and point out that a correspondence exists between the standard statistical analysis and the geometric inference problem. Then, we derive the "geometric AIC" and the "geometric MDL" as counterparts of Akaike's AIC and Rissanen's MDL. We show by experiments that the two criteria have contrasting characteristics in detecting degeneracy.
引用
收藏
页码:1307 / 1319
页数:13
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