A Systematic Approach for the Parameterisation of the Kernel-Based Hough Transform Using a Human-Generated Ground Truth

  • Jonas Lang
  • Mark Becke
  • Thomas Schlegl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9244)


Lines are one of the basic features that are used to characterise the content of an image and to detect objects. Unlike edges or segmented blobs, lines are not only an accumulation of certain feature pixels but can also be described in an easy and exact mathematical way. Besides a lot of different detection methods, the Hough transform has gained much attention in recent years. With increasing processing power and continuous development, computer vision algorithms get more powerful with respect to speed, robustness and accuracy. But there still arise problems when searching for the best parameters for an algorithm or when characterising and evaluating the results of feature detection tasks. It is often difficult to estimate the accuracy of an algorithm and the influences of the parameter selection. Highly interdependent parameters and preprocessing steps continually lead to only hardly comprehensible results. Therefore, instead of pure trial and error and subjective ratings, a systematic assessment with a hard, numerical evaluation criterion is suggested. The paper at hand deals with the latter ones by using a human-generated ground truth to approach the problem. Thereby, the accuracy of the surveyed Kernel-based Hough transform algorithm was improved by a factor of three. These results are used for the tracking of cylindrical markers and to reconstruct their spatial arrangement for a biomedical research application.


Feature detection Human-generated ground truth Hough transform Image processing Line detection Systematic parameterisation 


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  1. 1.
    Becke, M., Schlegl, T.: Toward an experimental method for evaluation of biomechanical joint behavior under high variable load conditions. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3370–3375 (2011)Google Scholar
  2. 2.
    Mukhopadhyay, P., Chaudhuri, B.B.: A Survey of Hough Transform. Pattern Recognition 48(3), 993–1010 (2015)CrossRefGoogle Scholar
  3. 3.
    Li, H., Lavin, M.A., Master, R.J.L.: Fast Hough Transform: A Hierarchical Approach. Computer Vision, Graphics, and Image Processing 36(2–3), 136–161 (1986)Google Scholar
  4. 4.
    Li, Q., Xie, Y.: Randomised Hough Transform With Error Propagation for Line and Circle Detection. Pattern Analysis and Applications 6(1), 55–64 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Kiryati, N., Eldar, Y., Bruckstein, A.: A Probabilistic Hough Transform. Pattern Recognition 24(4), 303–316 (1991)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Fernandes, L.A.F., Oliveira, M.M.: Real-time Line Detection Through an Improved Hough Transform Voting Scheme. Pattern Recognition 41(1), 299–314 (2008)CrossRefzbMATHGoogle Scholar
  7. 7.
    Akinlar, C., Topal C.: Real-time line segment detection by edge drawing. In: 18th IEEE International Conference on Image Processing (ICIP), pp. 2837–2840, September 2011Google Scholar
  8. 8.
    Montero A., Nayak A., Stojmenovic M., Zaguia N.: Robust line extraction based on repeated segment directions on image contours. In: Computational Intelligence for Security and Defense Applications (CSIDA), pp. 1–7 (2009)Google Scholar
  9. 9.
    von Gioi, R., Jakubowicz, J., Morel, J.-M., Randall, G.: LSD: A Fast Line Segment Detector with a False Detection Control. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(4), 722–732 (2010)CrossRefGoogle Scholar
  10. 10.
    Nguyen, T.T., Pham X.D., Kim, D., Jeon, J.W.: A test framework for the accuracy of line detection by hough transforms. In: 6th IEEE International Conference on Industrial Informatics (INDIN), pp. 1528–1533 (2008)Google Scholar
  11. 11.
    Guerreiro, R., Aguiar, P.: Incremental local hough transform for line segment extraction. In: 18th IEEE International Conference on Image Processing (ICIP), pp. 2841–2844 (2011)Google Scholar
  12. 12.
    Furukawa, Y., Shinagawa, Y.: Accurate and Robust Line Segment Extraction by Analyzing Distribution Around Peaks in Hough Space. Computer Vision and Image Understanding 92(1), 1–25 (2003)CrossRefGoogle Scholar
  13. 13.
    Du, S., Tu, C., van Wyk, B., Chen, Z.: Collinear segment detection using HT neighborhoods. IEEE Transactions on Image Processing, 3612–3620 (2011)Google Scholar
  14. 14.
    Dai, B., Pan, Y., Liu, H., Shi, D., Sun, S.: An Improved RHT algorithm to detect line segments. In: 2010 International Conference on Image Analysis and Signal Processing (IASP), pp. 407–410 (2010)Google Scholar
  15. 15.
    Martin, D., Fowlkes, C., Tal, D., Malik, J.: A Database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proc. 8th International Conference on Computer Vision, pp. 416–423 (2001)Google Scholar
  16. 16.
    Martin, D., Fowlkes, C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Transactions on Pattern Analysis and Machine Intelligence, 530–549 (2004)Google Scholar
  17. 17.
    Crevier, D.: Image Segmentation Algorithm Development Using Ground Truth Image Data Sets. Computer Vision and Image Understanding 112(2), 143–159 (2008)CrossRefGoogle Scholar
  18. 18.
    Attene, M., Katz, S., Mortara, M., Patane, G., Spagnuolo, M., Tal, A.: Mesh segmentation - a comparative study. In: IEEE International Conference on Shape Modeling and Applications (2006)Google Scholar
  19. 19.
    Benhabiles, H., Vandeborre, J.-P., Lavoue, G., Daoudi, M.: A Framework for the objective evaluation of segmentation algorithms using a ground-truth of human segmented 3D-models. In: IEEE International Conference on Shape Modeling and Applications (SMI), pp. 36–43 (2009)Google Scholar
  20. 20.
    Dutagaci, H., Cheung, C.P., Godil, A.: Evaluation of 3D Interest Point Detection Techniques via Human-generated Ground Truth. The Visual Computer 28(9), 901–917 (2012)CrossRefGoogle Scholar
  21. 21.
    Wang, S., Ge, F., Liu, T.: Evaluating edge detection through boundary detection. EURASIP Journal on Advances in Signal Processing, 213–227 (2006)Google Scholar
  22. 22.
    Lopez-Molina, C., De Baets, B., Bustince, H.: Quantitative Error Measures for Edge Detection. Pattern Recognition 46(4), 1125–1139 (2013)CrossRefGoogle Scholar
  23. 23.
    Yitzhaky, Y., Peli, E.: A Method for Objective Edge Detection Evaluation and Detector Parameter Selection. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(8), 1027–1033 (2003)CrossRefGoogle Scholar
  24. 24.
    Zhang, Z.: A Flexible New Technique for Camera Calibration. IEEE Transactions on Machine Intelligence 22(11), 1330–1334 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Regensburg Robotics Research Unit, Faculty of Mechanical EngineeringOstbayerische Technische Hochschule RegensburgRegensburgGermany

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