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J. Weng, T.S. Huang, N. Ahuja, "Motion and Structure from Line Correspondences; ClosedForm Solution, Uniqueness, and Optimization," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 3, pp. 318336, March, 1992.  
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@article{ 10.1109/34.120327, author = {J. Weng and T.S. Huang and N. Ahuja}, title = {Motion and Structure from Line Correspondences; ClosedForm Solution, Uniqueness, and Optimization}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {14}, number = {3}, issn = {01628828}, year = {1992}, pages = {318336}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.120327}, 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  Motion and Structure from Line Correspondences; ClosedForm Solution, Uniqueness, and Optimization IS  3 SN  01628828 SP318 EP336 EPD  318336 A1  J. Weng, A1  T.S. Huang, A1  N. Ahuja, PY  1992 KW  motion parameters; parameter estimation; necessary conditions; picture processing; pattern recognition; line correspondences; closedform solution; uniqueness; optimization; rigid scene; structure parameters; sufficient conditions; optimisation; parameter estimation; pattern recognition; picture processing VL  14 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
This work discusses estimating motion and structure parameters from line correspondences of a rigid scene. The authors present a closedform solution to motion and structure parameters from line correspondences through three monocular perspective views. The algorithm makes use of redundancy in the data to improve the accuracy of the solutions. The uniqueness of the solution is established, and necessary and sufficient conditions for degenerate spatial line configurations are given. Optimization has been employed to further improve the accuracy of the estimates in the presence of noise. Simulations have shown that the errors of the optimized estimates are close to the theoretical lower error bound.
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