Ellipse detection using sampling constraints

The ellipse is a fundamental shape in both natural and man-made objects and hence frequently encountered in images. Existing ellipse detection algorithms, viz., randomized Hough transform (RHT) and multi-population genetic algorithm (MPGA), have disadvantages. The RHT performs poorly with multiple ellipses and MPGA has a high false positive rate for complex images. The proposed algorithm selects random points using constraints of smoothness, distance and curvature. In the process of sampling, parameters of potential ellipses are progressively learnt to improve parameter accuracy. New probabilistic fitness measures are used to verify ellipses extracted: ellipse quality based on the Ramanujan approximation and completeness. Experiments on synthetic and real images show performance better than RHT and MPGA in detecting multiple, deformed, full or partial ellipses in the presence of noise and interference.