ISSN 0253-2778

CN 34-1054/N

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Open AccessOpen Access JUSTC Original Paper

Adaptive Hough transform based on sample distributions

  • Department of Automation, University of Science and Technology of China, Hefei 230027, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2015.01.008
  • Received Date: 15 May 2014
  • Accepted Date: 09 October 2014
  • Rev Recd Date: 09 October 2014
  • Publish Date: 30 January 2015
    Abstract

  • Abstract

    An adaptive Hough transform (AHT) method was proposed, which aims at reducing effects of the quantization unit of the parameter space on Hough transform(HT) in detecting line features. First, the sample model was built up by using samples and computing parameters of the model. Then, according to changes in the model parameters and sample distributions,the method was established to get the appropriate quantization parameters. Finally, the optimized quantization units were obtained and applied to feature extraction in a structured environment. The results show that the proposed method can optimize the quantization units, reduce the line detection error,and improve detection accuracy.

    Abstract

    An adaptive Hough transform (AHT) method was proposed, which aims at reducing effects of the quantization unit of the parameter space on Hough transform(HT) in detecting line features. First, the sample model was built up by using samples and computing parameters of the model. Then, according to changes in the model parameters and sample distributions,the method was established to get the appropriate quantization parameters. Finally, the optimized quantization units were obtained and applied to feature extraction in a structured environment. The results show that the proposed method can optimize the quantization units, reduce the line detection error,and improve detection accuracy.
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    • References(15)
  • [1]
    Castellanos J，Tardós J. Laser-based segmentation and localization for a mobile robot[C]// Proceedings of the 6th International Symposium on Robotics and manufacturing: Recent Trends in Research and Applications. New York, USA: IEEE Press, 1996, 6: 101-109.
    [2]
    Pfister S T，Roumeliotis S I，Burdick J W. Weighted line fitting algorithms for mobile robot map building and efficient data representation[C]// Proceedings of the IEEE International Conference on Robotics and Automation. New York, USA: IEEE Press, 2003: 1 304-1 311.
    [3]
    Jensfelt P, Christensen H. Laser based position acquisition and tracking in an indoor environment[C]// Proceedings of the International Symposium on Robotics and Automation. Gaithersburg, USA: IEEE Press, 1998, 1: 98-103.
    [4]
    He F, Fang Y C, WangY T, et al. Practical feature-based simultaneous localization and mapping using sonar data[C]// Proceeding of the 27th Chinese Control Conference. Kunming, China: IEEE Press, 2008: 421-425.
    [5]
    Bayro-Corrochano E, Bernal-Marin M. Generalized Hough transform and conformal geometric algebra to detect lines and planes for building 3D maps and robot navigation[C]// IEEE International Conference on Intelligent Robots and Systems. Taipei, China: IEEE Press, 2010: 810-815.
    [6]
    Boldt M, Weiss R, Riseman E. Token-based extraction of straight lines[J]. IEEE Transaction on System，Man and Cybernetics, 1989, 19(6): 1 581-1 594.
    [7]
    Lu Chuanguo，Feng Xinxi，Kong Yunbo，et al，Track initiation based on parallel Hough transform[J]. Journal of Radars, 2013, 2(3): 292-299.
    鹿传国, 冯新喜, 孔云波, 等. 并行 Hough 变换航迹起始[J]. 雷达学报, 2013, 2(3): 292-299.
    [8]
    Duan D G, Xie M, Mo Q, et al. An improved Hough transform for line detection[C]// International Conference on Computer Application and System Modeling. Taiyuan, China: IEEE Press, 2010, 2: 354-357.
    [9]
    Li H，Lavin M A，Le Master R J. Fast Hough transform: A hierarchical approach[J]. Computer Vision，Graphics， and Image Processing, 1986, 36(2): 139-161.
    [10]
    Xu L, Oja E. Randomized Hough transform (RHT): Basic mechanisms，algorithms，and computational complexities[J]. CVGIP： Image Understanding, 1993, 57(2): 131-154.
    [11]
    Xia D, Cha H, Xiao C S, et al. A new Hough transform applied in track initiation[C]// International Conference on Consumer Electronics, Communications and Networks. XianNing, China: IEEE Press, 2011: 30-33.
    [12]
    Han J H, Kóczy L, Poston T. Fuzzy Hough transform[J]. Pattern Recognition Letters, 1994, 15(7): 649-658.
    [13]
    Ebrahimpour R, Rasoolinezhad R, Hajiabolhasani Z, et al. Vanishing point detection in corridors: Using Hough transform and K-means clustering[J]. Computer Vision, 2012, 6(1): 40-51.
    [14]
    Guo Siyu，Zhai Wenjuan，Tang Qiu，et al. Combining the Hough transform and an improved least squares method for line detection[J]. Computer Science, 2012, 39(4): 196-200.
    郭斯羽, 翟文娟, 唐求, 等. 结合Hough变换与改进最小二乘法的直线检测[J]. 计算机科学, 2012, 39(4): 196-200.
    [15]
    Nguyen V, Gchter S, Martinelli A, et al. A comparison of line extraction algorithms using 2D range data for indoor mobile robotics[J]. Autonomous Robots, 2007, 23(2): 97-111.
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    [1]
    Castellanos J，Tardós J. Laser-based segmentation and localization for a mobile robot[C]// Proceedings of the 6th International Symposium on Robotics and manufacturing: Recent Trends in Research and Applications. New York, USA: IEEE Press, 1996, 6: 101-109.
    [2]
    Pfister S T，Roumeliotis S I，Burdick J W. Weighted line fitting algorithms for mobile robot map building and efficient data representation[C]// Proceedings of the IEEE International Conference on Robotics and Automation. New York, USA: IEEE Press, 2003: 1 304-1 311.
    [3]
    Jensfelt P, Christensen H. Laser based position acquisition and tracking in an indoor environment[C]// Proceedings of the International Symposium on Robotics and Automation. Gaithersburg, USA: IEEE Press, 1998, 1: 98-103.
    [4]
    He F, Fang Y C, WangY T, et al. Practical feature-based simultaneous localization and mapping using sonar data[C]// Proceeding of the 27th Chinese Control Conference. Kunming, China: IEEE Press, 2008: 421-425.
    [5]
    Bayro-Corrochano E, Bernal-Marin M. Generalized Hough transform and conformal geometric algebra to detect lines and planes for building 3D maps and robot navigation[C]// IEEE International Conference on Intelligent Robots and Systems. Taipei, China: IEEE Press, 2010: 810-815.
    [6]
    Boldt M, Weiss R, Riseman E. Token-based extraction of straight lines[J]. IEEE Transaction on System，Man and Cybernetics, 1989, 19(6): 1 581-1 594.
    [7]
    Lu Chuanguo，Feng Xinxi，Kong Yunbo，et al，Track initiation based on parallel Hough transform[J]. Journal of Radars, 2013, 2(3): 292-299.
    鹿传国, 冯新喜, 孔云波, 等. 并行 Hough 变换航迹起始[J]. 雷达学报, 2013, 2(3): 292-299.
    [8]
    Duan D G, Xie M, Mo Q, et al. An improved Hough transform for line detection[C]// International Conference on Computer Application and System Modeling. Taiyuan, China: IEEE Press, 2010, 2: 354-357.
    [9]
    Li H，Lavin M A，Le Master R J. Fast Hough transform: A hierarchical approach[J]. Computer Vision，Graphics， and Image Processing, 1986, 36(2): 139-161.
    [10]
    Xu L, Oja E. Randomized Hough transform (RHT): Basic mechanisms，algorithms，and computational complexities[J]. CVGIP： Image Understanding, 1993, 57(2): 131-154.
    [11]
    Xia D, Cha H, Xiao C S, et al. A new Hough transform applied in track initiation[C]// International Conference on Consumer Electronics, Communications and Networks. XianNing, China: IEEE Press, 2011: 30-33.
    [12]
    Han J H, Kóczy L, Poston T. Fuzzy Hough transform[J]. Pattern Recognition Letters, 1994, 15(7): 649-658.
    [13]
    Ebrahimpour R, Rasoolinezhad R, Hajiabolhasani Z, et al. Vanishing point detection in corridors: Using Hough transform and K-means clustering[J]. Computer Vision, 2012, 6(1): 40-51.
    [14]
    Guo Siyu，Zhai Wenjuan，Tang Qiu，et al. Combining the Hough transform and an improved least squares method for line detection[J]. Computer Science, 2012, 39(4): 196-200.
    郭斯羽, 翟文娟, 唐求, 等. 结合Hough变换与改进最小二乘法的直线检测[J]. 计算机科学, 2012, 39(4): 196-200.
    [15]
    Nguyen V, Gchter S, Martinelli A, et al. A comparison of line extraction algorithms using 2D range data for indoor mobile robotics[J]. Autonomous Robots, 2007, 23(2): 97-111.
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