A hybrid approach for ellipse detection in real images
Dilip Kumar Prasad, Maylor K. H. Leung · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
Extraction of elliptic shapes in real images is very challenging because the geometric shapes corresponding to the various objects often appear incomplete and deformed due to the presence of noise, cluttered background and occlusion by other objects. This paper proposes a new method of ellipse detection, which is able to deal with the challenges mentioned above, while being computationally efficient and more accurate than existing methods. The novelty of the current work is a grouping scheme based on a 'trust score' that indicates the trust that can be put upon an edge in a group. In the first stage, partial Hough transform is performed in order to generate the possible centers (or center bins in 2-dimensional pixel space). Then, a special histogram is generated using the 'trust score' that rates the relationship of the edge and the center bin. This histogram is used to group the edges and rank them within each group. In the second stage, least square technique is applied in order to judge and improve the grouping and finally find the parameters of the ellipses. Such hybrid method has various advantages like consideration of large number of possible groups, computational efficiency, parallelizability, real time application, etc. The method performs well for complicated real images and is suitable for real-time applications of machine vision.