IEEE Trans Image Process - Robust ellipse fitting based on sparse combination of data points.

Tópicos

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Resumo

Ellipse fitting is widely applied in the fields of computer vision and automatic industry control, in which the procedure of ellipse fitting often follows the preprocessing step of edge detection in the original image. Therefore, the ellipse fitting method also depends on the accuracy of edge detection besides their own performance, especially due to the introduced outliers and edge point errors from edge detection which will cause severe performance degradation. In this paper, we develop a robust ellipse fitting method to alleviate the influence of outliers. The proposed algorithm solves ellipse parameters by linearly combining a subset of ("more accurate") data points (formed from edge points) rather than all data points (which contain possible outliers). In addition, considering that squaring the fitting residuals can magnify the contributions of these extreme data points, our algorithm replaces it with the absolute residuals to reduce this influence. Moreover, the norm of data point errors is bounded, and the worst case performance optimization is formed to be robust against data point errors. The resulting mixed l1-l2 optimization problem is further derived as a second-order cone programming one and solved by the computationally efficient interior-point methods. Note that the fitting approach developed in this paper specifically deals with the overdetermined system, whereas the current sparse representation theory is only applied to underdetermined systems. Therefore, the proposed algorithm can be looked upon as an extended application and development of the sparse representation theory. Some simulated and experimental examples are presented to illustrate the effectiveness of the proposed ellipse fitting approach.

Resumo Limpo

ellips fit wide appli field comput vision automat industri control procedur ellips fit often follow preprocess step edg detect origin imag therefor ellips fit method also depend accuraci edg detect besid perform especi due introduc outlier edg point error edg detect will caus sever perform degrad paper develop robust ellips fit method allevi influenc outlier propos algorithm solv ellips paramet linear combin subset accur data point form edg point rather data point contain possibl outlier addit consid squar fit residu can magnifi contribut extrem data point algorithm replac absolut residu reduc influenc moreov norm data point error bound worst case perform optim form robust data point error result mix ll optim problem deriv secondord cone program one solv comput effici interiorpoint method note fit approach develop paper specif deal overdetermin system wherea current spars represent theori appli underdetermin system therefor propos algorithm can look upon extend applic develop spars represent theori simul experiment exampl present illustr effect propos ellips fit approach

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