Fits an ellipse to a set of points in "closed form" by minimizing algebraic least-squares error. The method used is
described in  and is a repartitioning of the solution describe in , with the aim of improving numerical
stability. The found ellipse is described using 6 coefficients, as is shown below.
F(x,y) = a*x^2 + 2*b*x*y + c*y^2 + 2*d*x + 2*e*y + f = 0 and b^2 - 4*ac < 0
One peculiarity of this algorithm is that it's less stable when perfect data is provided. This instability became evident when constructing unit tests and some of them failed. Tests on the original Matlab code also failed.
-  Radim Halir and Jan Flusser, "Numerically Stable Direct Least Squares Fitting Of Ellipses" 1998
-  Fitzgibbon, A. W., Pilu, M and Fischer, R. B.: "Direct least squares fitting of ellipses" Technical Report DAIRP-794, Department of Artificial Intelligence, The University of Edinburgh, January 1996
processFits the ellipse to the line
points- Set of points that are to be fit
weights- Weight or importance of each point. Each weight must be a positive number
- true if successful or false if it failed
getEllipsepublic EllipseQuadratic_F64 getEllipse()