FUZZY CELL HOUGH TRANSFORM FOR CURVE DETECTION

In this paper a new variation of Hough Transform is proposed. It can b e used to detect shapes or contours in an image, with better accuracy, especially in noisy images. The parameter space of Hough Transform is split into fuzzy cells which are defined as fuzzy numbers. This fuzzy split provides the advantage to use the uncertainty of the contour po int location which is increased when noisy images are used. By using f uzzy cells, each contour point in the spatial domain contributes in mo re than one fuzzy cell in the parameter space. The array that is creat ed after the fuzzy voting process is smoother than in the crisp case a nd the effect of noise is reduced. The curves can now be detected with better accuracy. The computation time that is slightly increased by t his method, can be minimized in comparison with classical Hough Transf orm, by using recursively the fuzzy voting process in a roughly split parameter space, to create a multiresolution fuzzily split parameter s pace. (C) 1997 Pattern Recognition Society. Published by Elsevier Scie nce Ltd.