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Uncertainty modeling and model selection for geometric inference.

Kenichi Kanatani
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Abstract

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.

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AlgorithmsArtificial IntelligenceComputer SimulationImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalInformation Storage and RetrievalModels, BiologicalModels, StatisticalNumerical Analysis, Computer-AssistedPattern Recognition, AutomatedReproducibility of ResultsSensitivity and SpecificitySignal Processing, Computer-AssistedStochastic ProcessesComparative StudyEvaluation StudyJournal ArticleResearch Support, Non-U.S. Gov'tValidation Study

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Kenichi Kanatani. 2004. Uncertainty modeling and model selection for geometric inference.. https://doi.org/10.1109/tpami.2004.93

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