A novel ellipse detection method for real images is proposed here. The method is novel in various respects. It uses a combination of geometric and Hough transform techniques for generating an initial guess of the centers of ellipses. This initial guess is used in a novel grouping technique in which the edges within a group are ranked in the order of their strength of relationship with the group. The novel relationship score is more selective and thus more efficient than the typical histogram count used in Hough transform methods. A least squares method has been adapted into an optimization scheme that reduces the number of outliers. The elliptic hypotheses are then subjected to non-heuristic saliency criteria. The thresholds for selection of elliptic hypotheses are determined by the detected hypotheses themselves, such that the selection is image independent and free of human intervention. Since only two control parameters are needed and the method requires a few seconds in most cases, it is suitable for practical applications. The experimental results for 1200 synthetic images and 400 real images from Caltech 256 dataset clearly demonstrate the superior accuracy and robustness of the proposed method as compared to contemporary ellipse detection methods.