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An information theoretic robust sequential procedure for surface model order selection in noisy range data

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2 Author(s)
Mirza, M.J. ; Dept. of Electr. Eng., Ohio State Univ., Columbus, OH, USA ; Boyer, K.L.

Modeling of the unknown surface, a key first step in the perception of surfaces in range images using the function approximation approach, is considered. Akaike's entropy-based information criterion (AIC) is a simple but powerful tool for choosing the best fitting model among several competing models. However, the AIC presupposes a fixed data set and a normality assumption on the error's distribution. The AIC is extended to a t-distribution noise model, which more realistically represents anomalies in the data such as outliers and quantization errors. This criterion is modified to be used with a robust sequential algorithm to accommodate the variable data size resulting from fitting different models. The modified criterion is applied to real range data, and its performance is compared with that of AIC and Consistent AIC

Published in:

Computer Vision and Pattern Recognition, 1992. Proceedings CVPR '92., 1992 IEEE Computer Society Conference on

Date of Conference:

15-18 Jun 1992