Abstract
A new and efficient version of the Hough Transform for curve detection, the Randomized Hough Transform (RHT), has been recently suggested. The RHT selects n pixels from an edge image by random sampling to solve n parameters of a curve and then accumulates only one cell in a parameter space. In this paper, the RHT is related to other recent developments of the Hough Transform by experimental tests in line detection. Hough Transform methods are divided into two categories: probabilistic and non-probablistic methods. Four novel extensions of the RHT are proposed to improve the RHT for complex and noisy images. These apply the RHT process to a limited neighborhood of edge pixels. Tests with synthetic and real-world images demonstrate the high speed and low memory usage of the new extensions, as compared both to the basic RHT and other versions of the Hough Transform.
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References
Ben-Tzvi, D., Sandler, M.B., “A Combinatorial Hough Transform,” Pattern Recognition Letters, vol. 11, no. 3, 1990, pp. 167–174.
Bergen, J.R., Shvaytser, H., “A Probabilistic Algorithm for Computing Hough Transforms,” J. of Algorithms, vol. 12., no. 4, 1991, pp. 639–656.
Duda, R.O., Hart, P.E., “Use of the Hough Transform To Detect Lines and Curves in Pictures,” Communications of the ACM, vol. 15, no. 1, 1972, pp. 11–15.
Hare, A.R., Sandler, M.B., “General Test Framework for Straight-Line Detection by Hough Transforms,” Proc. of IEEE Int. Symp. on Circuits and Systems IS-CAS'93, May 3–6, Chicago, USA, 1993, pp. 239–242.
Kiryati, N., Eldar, Y., Bruckstein, A.M., “A Probabilistic Hough Transform,” Pattern Recognition, vol. 24, no, 4., 1991, pp. 303–316.
Kälviäinen, H., Hirvonen, P., Xu, L., Oja, E., “Probabilistic and Non-probabilistic Hough Transforms: Overview and Comparisons,” Res. Rep. No. 45, Dept. of Inform. Techn., Lappeenranta Univ. of Techn., Lappeenranta, Finland, 1993. To appear in Image and Vision Computing.
Leavers, V.F., Ben-Tzvi, D., Sandler, M.B., “A Dynamic Combinatorial Hough Transform for Straight Lines and Circles,” Proc. of 5th Alvey Vision Conf., Reading, UK, September 25–28, 1989, pp. 163–168.
Leavers, V.F., “The Dynamic Generalized Hough Transform: Its Relationship to the Probabilistic Hough Transforms and an Application to the Concurrent Detection of Circles and Ellipses,” CVGIP: Image Understanding, vol. 56, no. 3, 1992, pp. 381–398.
V.F. Leavers, “Which Hough Transform?,” CVGIP: Image Understanding, vol. 58, no. 2, pp. 250–264, 1993.
Liang, P., “A New Transform for Curve Detection,” Proc. of Third Int. Conf. on Computer Vision, Osaka, Japan, December 1990, pp. 748–751.
Liang, P., “A New and Efficient Transform for Curve Detection,” J. of Robotic Systems, vol. 8, no. 6, 1991, pp. 841–847.
Princen, J., Yuen, H.K., Illingworth, J., Kittler, J., “A Comparison of Hough Transform Methods,” The Third Int. Conf. on Image Analysis and Its Applications, Conf. Pub. 307, Warwick, UK, July 1989, pp. 73–77.
Risse, T., “Hough Transformation for Line Recognition: Complexity of Evidence Accumulation and Cluster Detection,” CVGIP, vol. 46, 1989, pp. 327–345.
Roth, G., Levine, M.D., “Extracting Geometric Primitives,” Report TR-CIM-92-13, Computer Vision and Robotics Laboratory, McGill Research Centre for Intelligent Machines, McGill Univ., Montreal, Québec, Canada, October 1992.
Xu, L., Oja. E., Kultanen P., “A New Curve Detection Method: Randomized Hough Transform (RHT),” Pattern Recognition Letters, vol. 11, no. 5, 1990, pp. 331–338.
Xu, L., Oja, E., “Randomized Hough Transform (RHT): Basic Mechanisms, Algorithms, and Computational Complexities,” CVGIP: Image Understanding, vol. 57, no. 2, 1993, pp. 131–154.
Ylä-Jääski, A., Kiryati N, “Automatic Termination Rules for Probabilistic Hough Algorithms”, Proc. of 8th Scand. Conf. on Image Analysis, TromsØ, Norway, May, 1993, pp. 121–128.
Yuen, H.K., Princen, J., Illingworth, J., Kittler, J., “Comparative Study of Hough Transform Methods for Circle Finding,” Image and Vision Computing, vol. 8, no.1, 1990, pp. 71–77.
Yuen, K.S.Y., Lam, L.T.S., Leung, D.N.K., “Connective Hough Transform,” Image and Vision Computing, vol. 11, no. 5, 1993, pp. 295–301.
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© 1994 Springer-Verlag Berlin Heidelberg
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Kälviäinen, H., Hirvonen, P., Xu, L., Oja, E. (1994). Comparisons of probabilistic and non-probabilistic hough transforms. In: Eklundh, JO. (eds) Computer Vision — ECCV '94. ECCV 1994. Lecture Notes in Computer Science, vol 801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0028367
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DOI: https://doi.org/10.1007/BFb0028367
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