Uncertainty Modeling and Geometric Inference

نویسنده

  • Kenichi Kanatani
چکیده

We 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”. We point out that a correspondence exists between the standard statistical analysis and the geometric inference problem. We also compare the capability of the “geometric AIC” and the “geometric MDL” in detecting degeneracy.

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تاریخ انتشار 2003