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B. CernuschiFrias, D.B. Cooper, Y.P. Hung, P.N. Belhumeur, "Toward a ModelBased Bayesian Theory for Estimating and Recognizing Parameterized 3D Objects Using Two or More Images Taken from Different Positions," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 10, pp. 10281052, October, 1989.  
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@article{ 10.1109/34.42835, author = {B. CernuschiFrias and D.B. Cooper and Y.P. Hung and P.N. Belhumeur}, title = {Toward a ModelBased Bayesian Theory for Estimating and Recognizing Parameterized 3D Objects Using Two or More Images Taken from Different Positions}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {11}, number = {10}, issn = {01628828}, year = {1989}, pages = {10281052}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.42835}, 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  Toward a ModelBased Bayesian Theory for Estimating and Recognizing Parameterized 3D Objects Using Two or More Images Taken from Different Positions IS  10 SN  01628828 SP1028 EP1052 EPD  10281052 A1  B. CernuschiFrias, A1  D.B. Cooper, A1  Y.P. Hung, A1  P.N. Belhumeur, PY  1989 KW  3D object surface recognition; picturing processing; pattern recognition; Bayesian theory; parametric modeling; statistical estimation; parameter estimation; maximum likelihood estimation; Bayes methods; parameter estimation; pattern recognition; picture processing; statistical analysis VL  11 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
A parametric modeling and statistical estimation approach is proposed and simulation data are shown for estimating 3D object surfaces from images taken by calibrated cameras in two positions. The parameter estimation suggested is gradient descent, though other search strategies are also possible. Processing image data in blocks (windows) is central to the approach. After objects are modeled as patches of spheres, cylinders, planes and general quadricsprimitive objects, the estimation proceeds by searching in parameter space to simultaneously determine and use the appropriate pair of image regions, one from each image, and to use these for estimating a 3D surface patch. The expression for the joint likelihood of the two images is derived and it is shown that the algorithm is a maximumlikelihood parameter estimator. A concept arising in the maximum likelihood estimation of 3D surfaces is modeled and estimated. CramerRao lower bounds are derived for the covariance matrices for the errors in estimating the a priori unknown object surface shape parameters.
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