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Analytic curve detection from a noisy binary edge map using genetic algorithm

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Book cover Parallel Problem Solving from Nature — PPSN V (PPSN 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1498))

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Abstract

Currently Hough transform and its variants are the most common methods for detecting analytic curves from a binary edge image. However, these methods do not scale well when applied to complex noisy images where correct data is very small compared to the amount of incorrect data. We propose a Genetic Algorithm in combination with the Randomized Hough Transform, along with a different scoring function, to deal with such environments. This approach is also an improvement over random search and in contrast to standard Hough transform algorithms, is not limited to simple curves like straight line or circle.

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References

  1. Bergen, J. R., Shvaytser, H.: A probabilistic algorithm for computing Hough transforms. J. Algorithms, 12, 4 (1991) 639–656

    Article  MATH  MathSciNet  Google Scholar 

  2. Califano, A., Bolle, R. M., Taylor, R. W.: Generalized neighbourhoods: A newapproach to complex parameter feature extraction. Proc. IEEE Conference on Computer Vision and Pattern Recognition, (1989) 192–199

    Google Scholar 

  3. Cohen, M., Toussaint, G. T.: On the detection of structures in noisy pictures. Pattern Recognition, 9, (1977) 95–98

    Article  MATH  Google Scholar 

  4. Goldberg, D. E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, M.A. (1989)

    MATH  Google Scholar 

  5. Grimson, W. E. L., Huttenlocher, D. P.: On the sensitivity of the Hough transform for object recognition. IEEE Trans. Pattern Anal. Machine Intell., PAMI-12, (1990) 255–274

    Article  Google Scholar 

  6. Hill, A., Taylor, C. J.: Model-based image interpretation using genetic algorithms. Image and Vision Computing, 10, (1992) 295–300

    Article  Google Scholar 

  7. Hough, P. V. C.: Method and means for recognizing complex patterns. U.S. Patent No. 3069654 (1962)

    Google Scholar 

  8. Hunt, D. J., Nolte, L. W., Reibman, A. R., Ruedger, W. H.: Hough transform and signal detection theory performance for images with additive noise. Computer Vision, Graphics and Image Processing, 52, 3 (1990) 386–401

    Article  Google Scholar 

  9. Illingworth, J. and Kittler, J.: A survey of the Hough transform. Computer Vision, Graphics and Image Processing, 44, (1988) 87–116

    Article  Google Scholar 

  10. Kälviäinen, H., Xu, L., Oja, E.: Recent versions of the Hough transform and the randomized Hough transform: Overview and comparisons. Research Report No. 37, Department of Information Technology, Lappeenranta University of Technology, Finland (1993)

    Google Scholar 

  11. Kälviäinen, H., Hirvonen, P., Xu, L., Oja, E.: Probabilistic and non-probabilistic Hough transforms: overview and comparisons. Image and Vision Computing, 13, 4 (1995) 239–252

    Article  Google Scholar 

  12. Kälviäinen, H., Hirvonen, P., Oja, E.: Houghtool-a Software Package for Hough Transform Calculation. Proceedings of the 9th Scandinavian Conference on Image Analysis, Uppsala, Sweden, (June 1995) 841–848 (http://www.lut.fi/dep/tite/XHoughtool/xhoughtool.html)

    Google Scholar 

  13. Kälviäinen, H., Hirvonen, P.: Connective Randomized Hough Transform (CRHT). Proc. 9th. Scandinavian Conference on Image Analysis, Uppsala, Sweden (June 1995).

    Google Scholar 

  14. Kälviäinen, H., Hirvonen, P.: An extension to the Randomized Hough Transform exploiting connectivity. Pattern Recognition Letters, 18, 1 (1997) 77–85

    Article  Google Scholar 

  15. Kiryati, N., Eldar, Y., Bruckenstein, A.: A probabilistic Hough transform. Pattern Recognition, 24, 4 (1991) 303–316

    Article  MathSciNet  Google Scholar 

  16. Leavers, V. F.: Which Hough Transform? CVGIP: Image Understanding, 58, 2 (1993) 250–264

    Article  Google Scholar 

  17. Leavers, V. F.: It's probably a Hough: 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, 56, 3, (1992) 381–398

    Article  MATH  Google Scholar 

  18. Liang, P.: A new and efficient transform for curve deection. J. of Robotic Systems, 8, 6 (1991) 841–847

    Google Scholar 

  19. Maitre, H.: Contribution to the prediction of performances of the Hough transform. IEEE Trans. Pattern Anal. Machine Intell., PAMI-8, 5 (1986) 669–674

    Article  Google Scholar 

  20. Michalewicz, Z.: Genetic Algorithms + Data Structutes = Evolution Programs. Springer Verlag, Berlin (1992)

    Google Scholar 

  21. Princen, J., Illingworth, J., Kittler, J.: A formal definition of the Hough transform: properties and relationships. J. Math. Imaging Vision, 1, (1992) 153–168

    Article  Google Scholar 

  22. Risse, T.: Hough transform for the line recognition: complexity of evidence accumulation and cluster detection. Computer Vision, Graphics and Image Processing, 46, (1989) 327

    Article  Google Scholar 

  23. Roth, G., Levine, M. D.: Geometric primitive extraction using a genetic algorithm. IEEE Trans. Pattern Anal. Machine Intell., PAMI-16, 9 (1994) 901–905

    Article  Google Scholar 

  24. Shapiro, S. D.: Transformations for the computer detection of curves in noisy pictures. Computer Graphics Image Processing, 4, (1975) 328–338

    Google Scholar 

  25. Xu, L., Oja, E., Kultanen, P.: A new curve detection method: Randomized Hough transform (RHT). Pattern Recognition Letters, 11, 5 (1990) 331–338

    Article  MATH  Google Scholar 

  26. Xu, L., Oja, E.: Randomized Hough Transform (RHT): Basic mechanisms, algorithms, and computational complexities. CVGIP: Image Understanding, 57, 2 (1993) 131–154

    Article  Google Scholar 

  27. Yuen, K. S. Y., Lam, L. T. S., Leung, D. N. K.: Connective Hough Transform. Image and Vision Computing, 11, 5 (1993) 295–301

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, 208016, Kanpur, India

    Samarjit Chakraborty

  2. Department of Mechanical Engineering, Indian Institute of Technology Kanpur, 208016, Kanpur, India

    Kalyanmoy Deb

Authors
  1. Samarjit Chakraborty
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  2. Kalyanmoy Deb
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Agoston E. Eiben Thomas Bäck Marc Schoenauer Hans-Paul Schwefel

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© 1998 Springer-Verlag Berlin Heidelberg

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Chakraborty, S., Deb, K. (1998). Analytic curve detection from a noisy binary edge map using genetic algorithm. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN V. PPSN 1998. Lecture Notes in Computer Science, vol 1498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056856

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  • DOI: https://doi.org/10.1007/BFb0056856

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65078-2

  • Online ISBN: 978-3-540-49672-4

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