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The fitting of a collection of noisy data points to a circle is a nonlinear and challenging problem, and it plays an important role in many signal processing applications. This paper proposes a semi-definite programming solution for the circle fitting problem based on the semi-definite relaxation technique. The relaxation of the maximum likelihood estimation converts a nonconvex problem to an approximate...
The fitting of a number of noisy data points with a circle has found numerous applications in image processing and pattern recognition. This paper examines two methods to estimate the circle parameters: the Maximum Likelihood (ML) method and the Full-Least-Squares (FLS) method. The ML method is based on the noisy model from the data while the FLS method minimizes the geometric distance square. We...
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