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K.L. Boyer, M.J. Mirza, G. Ganguly, "The Robust Sequential Estimator: A General Approach and its Application to Surface Organization in Range Data," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 10, pp. 9871001, October, 1994.  
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@article{ 10.1109/34.329010, author = {K.L. Boyer and M.J. Mirza and G. Ganguly}, title = {The Robust Sequential Estimator: A General Approach and its Application to Surface Organization in Range Data}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {16}, number = {10}, issn = {01628828}, year = {1994}, pages = {9871001}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.329010}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  The Robust Sequential Estimator: A General Approach and its Application to Surface Organization in Range Data IS  10 SN  01628828 SP987 EP1001 EPD  9871001 A1  K.L. Boyer, A1  M.J. Mirza, A1  G. Ganguly, PY  1994 KW  function approximation; estimation theory; image segmentation; information theory; decision theory; robust sequential estimator; surface organization; range data; autonomous statistically robust sequential function approximation; parameterization; partially occluded surfaces; noisy outlierridden functional range data; sequential least squares; surface characterization techniques; surface hypotheses; parameter space; noisy depth map; unknown 3D scene; seed points; modified Akaike Information Criterion; prune stage; coincidental surface alignment; weighted voting scheme; 5/spl times/5 decision window; ambiguous point; majority consensus VL  16 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
Presents an autonomous, statistically robust, sequential function approximation approach to simultaneous parameterization and organization of (possibly partially occluded) surfaces in noisy, outlierridden (not Gaussian), functional range data. At the core of this approach is the Robust Sequential Estimator, a robust extension to the method of sequential least squares. Unlike most existing surface characterization techniques, the authors' method generates complete surface hypotheses in parameter space. Given a noisy depth map of an unknown 3D scene, the algorithm first selects appropriate seed points representing possible surfaces. For each nonredundant seed it chooses the best approximating model from a given set of competing models using a modified Akaike Information Criterion. With this best model, each surface is expanded from its seed over the entire image, and this step is repeated for all seeds. Those points which appear to be outliers with respect to the model in growth are not included in the (possibly disconnected) surface. Point regions are deleted from each newly grown surface in the prune stage. Noise, outliers, or coincidental surface alignment may cause some points to appear to belong to more than one surface. These ambiguities are resolved by a weighted voting scheme within a 5/spl times/5 decision window centered around the ambiguous point. The isolated point regions left after the resolve stage are removed and any missing points in the data are filled by the surface having a majority consensus in an 8neighborhood.
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