Randomised hough transform with error propagation for line and circle detection

In this paper, we introduce a new Randomised Hough Transform aimed at improving curve detection accuracy and robustness, as well as computational efficiency. Robustness and accuracy improvement is achieved by analytically propagating the errors with image pixels to the estimated curve parameters. The errors with the curve parameters are then used to determine the contribution of pixels to the accumulator array. The computational efficiency is achieved by mapping a set of points near certain selected seed points to the parameter space at a time. Statistically determined, the seed points are points that are most likely located on the curves and that produce the most accurate curve estimation. Further computational advantage is achieved by performing progressive detection. Examples of detection of lines using the proposed technique are given in the paper. The concept can be extended to non-linear curves such as circles and ellipses.