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Peter D. Lauren, N. Nandhakumar, "Estimating the Viewing Parameters of Random, Noisy Projections of Asymmetric Objects for Tomographic Reconstruction," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 5, pp. 417430, May, 1997.  
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@article{ 10.1109/34.589202, author = {Peter D. Lauren and N. Nandhakumar}, title = {Estimating the Viewing Parameters of Random, Noisy Projections of Asymmetric Objects for Tomographic Reconstruction}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {19}, number = {5}, issn = {01628828}, year = {1997}, pages = {417430}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.589202}, 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  Estimating the Viewing Parameters of Random, Noisy Projections of Asymmetric Objects for Tomographic Reconstruction IS  5 SN  01628828 SP417 EP430 EPD  417430 A1  Peter D. Lauren, A1  N. Nandhakumar, PY  1997 KW  Tomography KW  pose estimation KW  motion analysis KW  3D reconstruction. VL  19 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
Abstract—The ability to determine the viewing parameters of objects from their projections has enabled well established tomographic techniques to be employed in the threedimensional reconstruction of objects from images obtained via modalities where the orientation of the objects cannot be controlled. A method is described for the determination of the viewing parameters of randomly acquired projections of asymmetric objects. It extends upon the common lines algorithm by determining the relative orientation of projections from the location of lines of intersection among the Fourier transforms of the projections in threedimensional Fourier space. A new technique for finding the lines of intersection in the presence of translational displacement, and for subsequently finding the translational displacement, is presented. The complete algorithm is described and its efficacy is demonstrated using real data. A new technique for dealing with noise is also discussed.
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