Optimal motion and structure estimation
Weng, J.; Ahuja, N.; Huang, T.S.
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Volume 15, Issue 9, Sep 1993 Page(s):864 - 884
Digital Object Identifier 10.1109/34.232074
Summary:The causes of existing linear algorithms exhibiting various high
sensitivities to noise are analyzed. It is shown that even a small
pixel-level perturbation may override the epipolar information that is
essential for the linear algorithms to distinguish different motions.
This analysis indicates the need for optimal estimation in the presence
of noise. Methods are introduced for optimal motion and structure
estimation under two situations of noise distribution: known and
unknown. Computationally, the optimal estimation amounts to minimizing a
nonlinear function. For the correct convergence of this nonlinear
minimization, a two-step approach is used. The first step is using a
linear algorithm to give a preliminary estimate for the parameters. The
second step is minimizing the optimal objective function starting from
that preliminary estimate as an initial guess. A remarkable accuracy
improvement has been achieved by this two-step approach over using the
linear algorithm alone
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