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R. Hummel, V. Sundareswaran, "Motion Parameter Estimation from Global Flow Field Data," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 5, pp. 459476, May, 1993.  
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@article{ 10.1109/34.211466, author = {R. Hummel and V. Sundareswaran}, title = {Motion Parameter Estimation from Global Flow Field Data}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {15}, number = {5}, issn = {01628828}, year = {1993}, pages = {459476}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.211466}, 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  Motion Parameter Estimation from Global Flow Field Data IS  5 SN  01628828 SP459 EP476 EPD  459476 A1  R. Hummel, A1  V. Sundareswaran, PY  1993 KW  motion parameter estimation; global flow field data; vector flow field; imaging system; flow circulation algorithm; rotational parameters; FOE search; translational parameters; filtering; circularsurround zeromean receptive fields; quadratic polynomials; computer vision; filtering and prediction theory; motion estimation; parameter estimation; polynomials VL  15 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
Presented are two methods for the determination of the parameters of motion of a sensor, given the vector flow field induced by an imaging system governed by a perspective transformation of a rigid scene. Both algorithms integrate global data to determine motion parameters. The first (the flow circulation algorithm) determines the rotational parameters. The second (the FOE search algorithm) determines the translational parameters of the motion independently of the first algorithm. Several methods for determining when the function has the appropriate form are suggested. One method involves filtering the function by a collection of circularsurround zeromean receptive fields. The other methods project the function onto a linear space of quadratic polynomials and measures the distance between the two functions. The error function for the first two methods is a quadratic polynomial of the candidate position, yielding a very rapid search strategy.
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