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; process to detect non-overlapping ellipses from edge points; and procedure to detect cylinders from three-dimensional point clouds. The proposed methods were 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 the popular least median of squares method in both simulated and real-world datasets. The proposed process for detecting non-overlapping ellipses outperformed two established edge chaining/following methods, proposed by Fornaciari and Patraucean, in images. The proposed cylinder extraction method identified all detectable mechanical pipes in real-world point clouds. The results show promise for the application of the proposed methods for automatic extraction of circular targets from images and mechanical pipes from point clouds.