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J. Aisbett, "An Iterated Estimation of the Motion Parameters of a Rigid Body from Noisy Displacement Vectors," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 11, pp. 10921098, November, 1990.  
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@article{ 10.1109/34.61709, author = {J. Aisbett}, title = {An Iterated Estimation of the Motion Parameters of a Rigid Body from Noisy Displacement Vectors}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {12}, number = {11}, issn = {01628828}, year = {1990}, pages = {10921098}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.61709}, 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  An Iterated Estimation of the Motion Parameters of a Rigid Body from Noisy Displacement Vectors IS  11 SN  01628828 SP1092 EP1098 EPD  10921098 A1  J. Aisbett, PY  1990 KW  parameter set partitioning; recursive methods; iterated estimation; noisy displacement vectors; motion parameters; noisy point matches; perspective views; minimization; robust parameter estimation; rotary component; interframe differences; independently distributed errors; conditional generalized leastsquares formulas; iterative methods; minimisation; noise; parameter estimation; pattern recognition; picture processing VL  12 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
Concerns the estimation of the motion parameters of a rigid body from noisy point matches made on perspective views. A minimization problem which permits relatively robust parameter estimation and helps overcome poor zoom estimation when the fieldofview is small is formulated. It is assumed that the motion has a small rotary component, interframe differences are small, and the errors in the system are due to independently distributed errors in the components of the displacement vectors. A fast procedure for minimization is exposited, in which the parameter set is partitioned and conditional generalized leastsquares formulas identified. Recursive application of these provides the search space for the minimization problem. Comparative results are presented using simulated data and displacement vectors obtained from an intensitybased matching algorithm.
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