This manuscript provides a collection of new methods for the automated detection of non-overlapping ellipses from edge points. The new methods include a robust Monte Carlo-based approach for detecting points following elliptical patterns from outliers; process to detect non-overlapping ellipses from edge points; and procedure to detect cylinders from three-dimensional point clouds. The proposed methods were thoroughly compared with established state-of-the-art methods, using simulated and real-world datasets, through the design of four sets of original experiments. It was found that the proposed robust ellipse detection was superior to four reliable robust methods, including the popular least median of squares, in both simulated and real-world datasets. The proposed process for detecting non-overlapping ellipses outperformed three established methods, proposed by Fornaciari, Patraucean, and Pangiotakis, in images . The proposed cylinder extraction method identified all detectable mechanical pipes in  real-world point clouds  . The results of this investigation show promise for the application of the proposed methods for automatic extraction of circular targets from images and pipes from point clouds.
This manuscript provides a collection of new methods for the automated detection of non-overlapping ellipses from edge points. The methods introduce new developments in: (i) robust Monte Carlo-based ellipse fitting to 2-dimensional (2D) points in the presence of outliers; (ii) detection of non-overlapping ellipse from 2D edge points; and (iii) extraction of cylinder from 3D point clouds. The proposed methods were thoroughly compared with established state-of-the-art methods, using simulated and real-world datasets, through the design of four sets of original experiments. It was found that the proposed robust ellipse detection was superior to four reliable robust methods, including the popular least median of squares, in both simulated and real-world datasets. The proposed process for detecting non-overlapping ellipses achieved F-measure of 99.3\% on real images, compared to F-measures of 42.4\%, 65.6\%, and 59.2\%, obtained using the methods of Fornaciari, Patraucean, and Panagiotakis, respectively . The proposed cylinder extraction method identified all detectable mechanical pipes in two real-world point clouds , obtained under laboratory, and industrial construction site conditions . The results of this investigation show promise for the application of the proposed methods for automatic extraction of circular targets from images and pipes from point clouds.
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new methods include a
methods introduce new developments in: (i)
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approach for detecting points following elliptical patterns from outliers; process to detect
ellipse fitting to 2-dimensional (2D) points in the presence of outliers; (ii) detection of
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ellipses from
ellipse from 2D
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procedure to detect cylinders from three-dimensional
(iii) extraction of cylinder from 3D
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outperformed three established methods, proposed by
achieved F-measure of 99.3\% on real images, compared to F-measures of 42.4\%, 65.6\%, and 59.2\%, obtained using the methods of
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Pangiotakis, in images
Panagiotakis, respectively
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two
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, obtained under laboratory, and industrial construction site conditions