Comparisons of probabilistic and non-probabilistic hough transforms

  • Heikki Kälviäinen
  • Petri Hirvonen
  • Lei Xu
  • Erkki Oja
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 801)


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.


Edge Point Edge Pixel Line Parameter Line Detection Hough Transform 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Heikki Kälviäinen
    • 1
  • Petri Hirvonen
    • 1
  • Lei Xu
    • 2
  • Erkki Oja
    • 3
  1. 1.Department of Information TechnologyLappeenranta University of TechnologyLappeenrantaFinland
  2. 2.Department of Computer ScienceChinese University of Hong KongShatinHong Kong
  3. 3.Laboratory of Information and Computer ScienceHelsinki University of TechnologyEspooFinland

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