for a machine vision project I am trying to search image data for quadratic surfaces (f(x,y) = Ax^2+Bx+Cy^2+Dy+Exy+F). My plan is to iterate through regions of data and perform a surface-fit, look at the error, see if it's a continuous surface (which would probably indicate a feature in the image).
I was previously able to find quadratic curves (f(x) = Ax^2+Bx+C) in the image data by sampling lines, by using the equations on this site
http://mathforum.org/library/drmath/view/72047.html
this worked well, was promising, but it would be much more useful for my task to find 2-D regions that form continuous surfaces.
I see lots of articles indicating that least squares regressions scales up to multiple dimensions, but I'm not able to find code for this Hopefully there is a "closed form" (non-iterative, just compute from your data points) solution, like described above for 1D data. Does anybody know of some source or pseudocode that accomplishes this? Thanks.
(Sorry if my terminology is a bit off.)