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Geometric information criterion for model selection
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DOI：10.1023/A:1007948927139.png)
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摘要
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In building a 3-D model of the environment from image and sensor data, one must fit to the data an appropriate class of models, which can be regarded as a parametrized manifold, or geometric model, defined in the data space. In this paper, we present a statistical framework for detecting degeneracies of a geometric model by evaluating its predictive capability in terms of the expected residual and derive the geometric AIC. We show that it allows us to detect singularities in a structure-from-motion analysis without introducing any empirically adjustable thresholds. We illustrate our approach by simulation examples. We also discuss the application potential of this theory for a wide range of computer vision and robotics problems.
Keyword:
model selection
degeneracy detection
statistical estimation
AIC
maximum likelihood estimation
structure from motion
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