Abstract
Ellipse fitting aims at constructing an elliptical equation that best fits the scattering points collected from an edge detection process. However, the edge detection process may introduce some noisy scattering points. This paper proposes a robust ellipse fitting model based on the Lagrange programming neural network (LPNN) framework. We formulate the ellipse fitting problem as a constrained optimization problem. The objective function contains an \(\ell _1\)-norm term which can effectively suppress the effect of outliers. Since the LPNN framework cannot handle non-differentiable objective functions, we introduce an approximation for the \(\ell _1\)-norm term. Besides, the local stability of the proposed LPNN method is discussed. Simulation results show that the proposed ellipse fitting algorithm can effectively reduce the influence of outliers.
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