Combining Feature-based and Model-based Approaches For Robust Ellipse Detection

Abstract

Fast and robust ellipse detection is a vital step in many image processing and computer vision applications. Two main approaches exist for ellipse detection, i.e., model-based and feature-based. Model-based methods require much more computation, but they can perform better in occlusions. Feature-based approaches are fast but may perform insufficient in cluttered cases. In this study, we propose an hybrid method which combines both approaches to accelerate the process without compromising accuracy. We extract elliptical arcs to narrow down search space by obtaining seeds for prospective ellipses. For each seed arc, we compute a limited search region consisting of hypothetical ellipses that each can be formed with that seed. Later, we vote them on the edge image to determine best hypothesis among the all, if exists. We tested the proposed algorithm on a public dataset and promising results are obtained compare to state of the art methods in the literature.