Consider the biquadratic representation of an ellipse:

Given *n* noisy points ( ), we want to
estimate the coefficients of the ellipse: .
Due to the homogeneity, we set .

For each point , we thus have one scalar equation:

where

Hence, can be estimated by minimizing the following objective function (weighted least-squares optimization)

where 's are positive weights.

Assume that each point has the same error distribution with mean zero and covariance . The covariance of is then given by

where

Thus we have

The weights can then be chosen to the inverse proportion of the variances. Since multiplication by a constant does not affect the result of the estimation, we set

The objective function (16) can be rewritten as

which is a quadratic form in unit vector . Let

The solution is the eigenvector of associated to the smallest eigenvalue.

If each point is perturbed by noise of with

the matrix is perturbed accordingly: , where
is the unperturbed matrix.
If , then the estimate is *statistically unbiased*; otherwise,
it is *statistically biased*, because following the perturbation theorem
the bias of , i.e., .

Let , then . We have

If we carry out the Taylor development and ignore quantities of order higher than 2, it can be shown that the expectation of is given by

It is clear that if we define

then is unbiased, i.e., , and hence
the unit eigenvector of
associated to the smallest eigenvalue is an *unbiased*
estimate of the exact solution .

Ideally, the constant *c* should be chosen so that ,
but this is impossible unless image noise characteristics are known. On the
other hand, if , we have

because takes its absolute minimum 0 for
the exact solution in the absence of noise. This suggests that
we require that at each iteration. If for the
current *c* and , , we can update *c* by such that

That is,

To summarize, the renormalization procedure can be described as:

- Let
*c=0*, for . - Compute the unit eigenvector of
associated to the smallest eigenvalue, which is denoted by .

- Update
*c*asand recompute using the new .

- Return if the update has converged; go back to step 2 otherwise.

** Remark 1:** This implementation is different from that described in
the paper of Kanatani [9].
This is because in his implementation, he uses the N-vectors to represent the
2-D points. In the derivation of the bias, he assumes that the perturbation in
each N-vector, i.e., in his notations, has the same
magnitude .
This is an unrealistic assumption. In fact, to the first order,
thus
.
Hence,
,
where we assume the perturbation in image plane is the same for each point
(with mean zero and standard deviation ).

** Remark 2:** This method is optimal only in the sense of

Thu Feb 8 11:42:20 MET 1996