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. Author manuscript; available in PMC: 2009 Oct 15.
Published in final edited form as: Int J Comput Vis. 2008 Feb 1;76(2):183–204. doi: 10.1007/s11263-007-0050-3

Fig. 9.

Fig. 9

a The likelihood (frequency) of convergence plot against the magnitude of a random perturbation to the ground-truth fitting results computed by tracking through a trinocular sequence. The results show that the calibrated multi-view algorithms are more robust than the uncalibrated multi-view algorithm discussed in Sect. 3, which itself is more robust than the 2D+3D single-view algorithm (Xiao et al. 2004a). b The rate of convergence is estimated by plotting the average error after each iteration against the iteration number. The results show that the calibrated multi-view algorithms converge faster than the uncalibrated algorithm, which converges faster than the single-view 2D+3D algorithm