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S. Sullivan, L. Sandford, J. Ponce, "Using Geometric Distance Fits for 3D Object Modeling and Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 12, pp. 11831196, December, 1994.  
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@article{ 10.1109/34.387489, author = {S. Sullivan and L. Sandford and J. Ponce}, title = {Using Geometric Distance Fits for 3D Object Modeling and Recognition}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {16}, number = {12}, issn = {01628828}, year = {1994}, pages = {11831196}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.387489}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Using Geometric Distance Fits for 3D Object Modeling and Recognition IS  12 SN  01628828 SP1183 EP1196 EPD  11831196 A1  S. Sullivan, A1  L. Sandford, A1  J. Ponce, PY  1994 KW  object recognition; solid modelling; algebra; deformation; geometric distance fits; 3D object modeling; 3D object modeling and recognition; algebraic surface models; automatic model construction; pose computation; motion estimation; deformation estimation; constrained optimization; nonlinear leastsquares estimation techniques; meansquared geometric distance minimization; point set; ray set; parameterized surface; unknown parameters; surface coefficients; transformation mapping; coordinate system; range images; computerized tomography images; video images; implicit algebraic surfaces VL  16 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
Addresses the problems of automatically constructing algebraic surface models from sets of 2D and 3D images and using these models in pose computation, motion and deformation estimation, and object recognition. We propose using a combination of constrained optimization and nonlinear leastsquares estimation techniques to minimize the meansquared geometric distance between a set of points or rays and a parameterized surface. In modeling tasks, the unknown parameters are the surface coefficients, while in pose and deformation estimation tasks they represent the transformation which maps the observer's coordinate system onto the modeled surface's own coordinate system. We have applied this approach to a variety of real range, computerized tomography and video images.
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