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Juno PDS data
Bjorn Jonsson
post Jun 8 2018, 12:16 PM
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QUOTE (Gerald @ Jun 7 2018, 11:42 PM) *
Any of my attempts to define a globally accurate lense distortion model on the basis of Brown-Conrady with only 4 free parameters failed thus far. Locally it's no problem to go below 1/100 pixel accuracy with respect to some error metrics. But I would have been surprised, if Brown-Conrady would have worked so easily for global lense geometry. With an increasing number of non-zero parameters, the Brown-Conrady model becomes more and more unstable and unrealistic. That issue is well-known since several decades, at least, (probably more than a century in some cases) for other approximations derived from Taylor polynomials. (Think, e.g. of splines of polynomials of degree 7 rather than cubic splines. They oscillate too much to be useful in practice.) Therefore, I'll consider approaches rather different from Brown-Conrady. But I can't tell which of the long list of those more or less straightforward alternatives will be as successful as required, before it's elaborated and tested. Those methods have their own specific issues.

I can imagine it might be difficult to find a mathematical function that works well everywhere if you want this level of accuracy - there could be various minor 'irregularities'. For example minor distortions might not be perfectly symmetrical around the optical axis at this level of accuracy.

I wonder if it might make sense to measure control points using stars in JunoCam images and then triangulate the control points. Another set of triangulated control points would contain the known, correct (i.e. without optical distortions) positions of the stars. You could then determine an affine transform for each triangle and use this to map from distorted pixel positions to non-distorted positions or vice versa. A possible drawback is that you might need a lot of control points (i.e. small triangles) for sufficient accuracy.
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post Jun 8 2018, 02:38 PM
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Sorry for not having followed all the details in this thread, but what do you use to fit the B-C parameters? Are you finding the best-fit parameters from images of Jupiter using its modeled shape/position? Or are you using images that show stars? It seems the latter would avoid any modeling uncertainties in the shape of Jupiter since the stars' true directions are precisely known.
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post Jun 8 2018, 03:07 PM
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QUOTE (fredk @ Jun 8 2018, 06:38 AM) *
Sorry for not having followed all the details in this thread, but what do you use to fit the B-C parameters?

The Junocam I kernel parameters were fit using stars from the cruise imaging and validated using the positions of the galilean satellites during PJ1.

How Gerald's processing works with regard to camera parameters has never been clear to me.

I am personally of the opinion that expecting deep sub-pixel registration between bands is a pipe dream, but obviously I could be wrong.

Disclaimer: This post is based on public information only. Any opinions are my own.
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