On improving the accuracy of the Hough transform: theory, simulations, and experiments

The authors present two methods for very-high-precision estimation of straight-line parameters from the Hough transform and compare them with the standard method of taking the absolute peak in the Hough array and with least-squares fitting using both extensive simulation and a number of tests with real target images. Both methods use preprocessing and interpolation in the Hough array, and are based on compensating for effects that cause a spreading of the peak in Hough space. By interpolation, the authors achieve accuracy better than the accumulator cell size. A complete set of simulations show that the two methods produce similar results, which are much better than taking the absolute peak in Hough space. They also compare well with least-square fitting, which was considered optimal in the case of zero mean noise. Results of experiments with real images are reported, confirming that the Hough transform can yield very accurate results, almost as good as least-squares fitting for zero mean noise