Brent wrote:

> Pablo,
>    Now that I think about it, there is something odd in the fact that
> doing a multi-step optimization by changing which variables are
> optimized works any better than a full optimization.

Maybe my explanation in the previous email was a bit misleading. What I 
meant is to start optimizing with optimizing two images (with good 
connections etc.), then add the third, optimize again, add the fourth 
etc. This is done by the incremental optimization in hugin. (Note that 
here, control points in the non-optimized images are ignored).

>  That is, if the
> current solution is in a local minima with respect to 10 variables,
> for example, then it will still be in a local minima if fewer than the
> full number is optimized starting from that point.

However, there might be other local minima, which can also be found by 
the optimizer, as its way though the solution space is changed, and some 
"shortcuts" might be blocked (such as modifying lens distortions and d,e 
shift to make images fit better during the first iterations), might send 
the iterative optimization into the wrong direction, from which it might 
be unable to recover. Then the first local minima of will be different, 
and thus later optimization with more parameters will also find another 
solution.

 > If there's a way
> out of trap when optimizing a subset of the variables, then that same
> way out is available when more variables are allowed to vary.

Hmm, I don't think there are always ways of of local minimas, and adding 
some new parameters to the optimisation do not nessecarily mean that the 
previous local minima ceases to be one in the new parameter space.

ciao
   Pablo


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