Abstract
Least squares based ellipse detection is used as a core process in many image processing applications. In order to restrict the solution to ellipses and avoid non-elliptic conics, constrained optimization has to be incorporated in the least squares model. This paper proposes a least squares method that does not require a constrained optimization and has very low false positive rates. In contrast to the algebraic model of conics, we use the geometric model of ellipse and minimize the geometric distance of the fitted ellipse from the digital curve. As a result, the solutions are strictly restricted to ellipses. Results demonstrate a superior performance than most least squares based method for elliptic curves and greater true negative rates for non-elliptic curves even in presence of 30% noise.
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Prasad, D.K., Quek, C., Leung, M.K.H. (2012). A Precise Ellipse Fitting Method for Noisy Data. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2012. Lecture Notes in Computer Science, vol 7324. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31295-3_30
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DOI: https://doi.org/10.1007/978-3-642-31295-3_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31294-6
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