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Ellipse Fitting Based on Neural Network Optimization

Ellipse fitting is to estimate the parameters of the ellipse from a set of coplanar points, but the geometric distance from the plane point to the elliptic curve is difficult to calculate directly. This paper proposes a neural network optimization-based algorithm to estimate the geometric distances from plane points to elliptic curves to solve the ellipse fitting problem. Specifically, the neural network is firstly applied to train the geometric distance between the plane point and the standard ellipse, and then obtain the geometric distance between the plane point and the general ellipse through the rotation and translation transformation. Moreover, use minimizing the sum of the Euclidean distances from the plane point to the elliptic curve as the objective function, and finally use the gradient descent method to iteratively solve the ellipse parameters. Consequently, the proposed method avoids the direct algebraic solution of the geometric distance from the plane point to the elliptic curve and the performance of the algorithm together with its robustness to noise are analysed via numerical simulation.