Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
권호 표지

A Self-Organizing Map Based Hough Transform for Detecting Straight Lines

직선 추출을 위한 자기조직화지도 기반의 허프 변환

  • Lee, Moon-Kyu (Faculty of Mechanical and Automotive Engineering, Keimyung University)
  • 이문규 (계명대학교 기계자동차공학부 산업공학전공)
  • Published : 2002.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Detecting straight lines in an image is frequently required for various machine vision applications such as restoring CAD drawings from scanned images and object recognition. The standard Hough transform has been dominantly used to that purpose. However, massive storage requirement and low precision in estimating line parameters due to the quantization of parameter space are the major drawbacks of the Hough transform technique. In this paper, to overcome the drawbacks, an iterative algorithm based on a self-organizing map is presented. The self-organizing map can be adaptively learned such that image points are clustered by prominent lines. Through the procedure of the algorithm, a set of lines are sequentially detected one at a time. The algorithm can produce highly precised estimates of line parameters using very small amount of storage memory. Computational results for synthetically generated images are given. The promise of the algorithm is also demonstrated with its application to two natural images of inserts.

Keywords

References

  1. Bhattacharya, P., Liu, H., Rosenfield, A. and Thompson, S. (2000), Hough-Transform Detection of Lines in 3-D Space, Pattern Recognition Letters, 21, 843-849
  2. Chutatape, O. and Guo, L. (1999), A Modified Hough Transform for Line Detection and its Performance, Pattern Recognition, 32, 181-192
  3. Dori, D. (1997), Orthogonal Zig-Zag: an Algorithm for Vectorizing Engineering Drawings Compared with Hough Transform, Advances in Engineering Software, 28, 11-24
  4. Hough, P. V. C. (1962), Method and Means for Recognizing Complex Pattern, U.S. Patent No. 3069654
  5. Illingworth, J. and Kittler, J. (1987), The Adaptive Hough Transform, IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(5), 690-698 https://doi.org/10.1109/TPAMI.1987.4767964
  6. Illingworth, J. and Kittler, J. (1988), A Survey of the Hough Transform, Computer Vision, Graphics, and Image Processing, 44, 87-116
  7. Ioannou, D., Huda, W. and Laine, A. F. (1999), Circle Recognition through a 2D Hough Transform and Radius Histogramming, Image and Vision Computing, 12, 15-26
  8. Jia, X. and Nixon, M. S. (1995), Extending the feature Vector for Automatic Face recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(12), 1167-1176 https://doi.org/10.1109/34.476509
  9. Ker, J.-I., Chen, F. F. and Lu, J. (1996), A Quick Hough Transform Parameter Search Algorithm for Flexible Part Inspection, Journal of Manufacturing Systems, 15(6), 404-418 https://doi.org/10.1016/S0278-6125(97)83054-3
  10. Kierkegaard, P. (1992), A Method for Detection of Circular Arcs based on the Hough Transform, Machine Vision and Applications, 5, 248-263
  11. Kohonen, K. (1982), Self-Organized Formation of Topologically Correct Feature Maps, Biological Cybernetics, 43, 59-69
  12. Leavers, V. F. (1993), Survey-Which Hough Transform, Computer Vision, Graphics, and Image Processing-Image Understanding, 58, 250-264
  13. Lee, M.-K. (1994), FLASOM-Facility Layout by a Self-Organizing Map, Journal of the Korean Institute of Industrial Engineers, 20(2), 65-76
  14. Li, H., Lavin, M. A. and LeMaster, R. J. (1986), Fast Hough Transform: A Hierarchical Approach, Computer Vision, Graphics and Image Processing, 36, 139-161
  15. McMullen, P. R. (2001), A Kohonen Self-Organizing Map Approach to Addressing a Multiple Objective, Mixed-Model JIT Sequencing Problem, International Journal of Production Economics, 72, 59-71
  16. Orlov, A. A., Sadykov, S. S. and Zhiznyakov, A. L. (2000), Using the Hough Transform for Extraction and Suppression of Ribs in X-rays of the Thorax, Pattern Recognition and Image Analysis, 25(2) 365-367
  17. Palmer, P. L., Kittler, J. and Petrou, M (1995), An optimizing Line Finder Using a Hough Transform Algorithm, Computer Vision, and Image Understanding, 17(12), 1167-1176
  18. Pan, H.-L. and Chen, Y.-C. (1992), Liver Tissues Classification by Artificial Neural Networks, Pattern Recognition Letters. 13, 335-368, 1992
  19. Park, J. W. and Kim, K. D. (1992), A Study on Efficient Image Processing and CAD-Vision System Interface, Journal of the Korean Institute of Industrial Engineers, 18(2), 11-22
  20. Rhee, J. W. and Rhee, J. T. (1996), Collision-Free Path Planning for Robot Manipulator using SOM, Journal of the Korean Institute of Industrial Engineers, 22(3), 499-515
  21. Ritter, H., Martinetz, T. and Schulten, K. (1992), Neural Computation and Self-Organizing Maps: An Introduction, Addison Wesley, USA
  22. Tian, Y. T. and Shah, M. (1997), Recovering 3D Motion of Multiple Objects Using Adaptive Hough Transform, IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(10), 1178-1183 https://doi.org/10.1109/34.625131
  23. Tsai, D.-M. (1996), Detecting and Locating Partly Occluded Planar Parts by Clustering, lIE Transactions, 28, 531-544
  24. Yang, M. C. K. and Lee, J.-S. (1997), Hough Transform Modified by Line Connectivity and Line Thickness, IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(8),905-909 https://doi.org/10.1109/34.608293