Chui, C. K. and G. Chen. Kalman ltering with real time applications. Springer, Berlin-Heidelberg-New York, 1987. a. normalized data. b. Algebraic tting. c. Taubin's tting. d. Euclidean tting. Fig. 3. Fitting results for a real range data ( points). The normalized data set was shifted by t = [0:3; 0:2; 0:1] and rotated by # = =12 and n = [0:5; 1:0; 0:5].  Home/Search   Document Not in Database   Summary   Related Articles

.... Range Analysis) project, an EC TMR network (ERB FMRX CT97 0127) 2 Least squares tting of general curves and surfaces Parameter estimation, usually cast as an optimization problem, can be divided into three general techniques: least squares tting (e.g. 1, 3, 9, 10, 12] Kalman ltering (e.g. [4, 5]) and robust techniques (e.g. 2, 6] Given a nite set of data points D = fx p g, p 2 [1; P ] the problem of tting a general curve and surface Z(f) to D by a least squares method is to minimize a distance measure 1 P P X p=1 dist (x p ; Z(f) Minimum (2) from the data points x p to ....

Chui, C. K. and G. Chen. Kalman ltering with real time applications. Springer, Berlin-Heidelberg-New York, 1987. a. normalized data. b. Algebraic tting. c. Taubin's tting. d. Euclidean tting. Fig. 3. Fitting results for a real range data ( points). The normalized data set was shifted by t = [0:3; 0:2; 0:1] and rotated by # = =12 and n = [0:5; 1:0; 0:5].

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