Fiting a surface of best fit to a set of data points

AI Thread Summary
To fit a surface to a 1920x1080 grid of data points representing a uniform light source, a least squares approach is recommended. The user successfully applied the least squares method to model the surface as z = Ax + By + C, creating a matrix of sums for calculations. This method proved effective for both 3D and 2D cases. For those seeking software solutions, resources from MathWorks may be beneficial. Overall, the least squares method is a suitable technique for this type of data fitting.
Woland
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Hello all,

I am trying to fit a surface to a 1920x1080 grid of evenly spaced data points. The values are supposed to be more or less uniform (its an image of a uniform white light source). So I would like to fit a plane to it (but maybe a paraboloid if it is not quiete uniform). What method should I use? A least squares approach? I can't seem to find much on google for this. Does anyone have an approach, or perhaps could recommend some literature which covers this?

Thanks!
 
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I was able to figure it out . The usual least squares method applies to this. I fit z = Ax + By + C by creating a matrix of sums etc etc. It worked out nicely for the 3D case, same as for the 2D.

Thank you,
 
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