Fragmental ellipse fitting based on least square algorithm

The fragmental ellipse fitting algorithm based on least square is studied. Although the geometric fitting of ellipse offers high accuracy, the iteration process makes this fitting algorithm sensitive to the initial parameters. With very scattered data and high noise level, the simple linear fitting often yields unbounded hyperbola, a failed fitting. In contrast, the ellipse-constraint algebraic fitting always provides an elliptical solution and gives a proper initial estimate. Considering the parameter constraints on the position and shape of the ellipse, robust fitting result is obtained even for very short ellipse arc and high noise level. Simulation result and real image fitting are presented to validate the algorithm.