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ARG Based on Arcs and Segments to Improve the Symbol Recognition by Genetic Algorithm

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Graphics Recognition. Recent Advances and New Opportunities (GREC 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5046))

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Abstract

A genetic matching algorithm is extended to take into account primitive arcs in a pattern recognition process. Usually approaches based on segments are sensitive to over segmentation effects. Handling with more accurate description allows to improve the recognition by limiting the number of vertices to be matched and so to decrease processing time. Experimental studies using real data attest the robustness of the proposed method.

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References

  1. Okazaki, A., Kondo, T., Mori, K., Tsunekawa, S., Kawamoto, E.: An Automatic Circuit Diagram Reader with Loop-Structure-Based Symbol Recognition. IEEE Transactions on PAMI 10(3), 331–341 (1988)

    Google Scholar 

  2. Lladós, J., Valveny, E., Sánchez, G., Martí, E.: Symbol Recognition: Current Advances and Perspectives. In: Blostein, D., Kwon, Y.-B. (eds.) GREC 2001. Lladós, J., Valveny, E., Sánchez, G., Martí, vol. 2390, pp. 104–127. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  3. Bunke, H.: Error Correcting Graph Matching: On the Influence of the Underlying Cost Function. IEEE Transactions on PAMI 21(9), 917–922 (1999)

    Google Scholar 

  4. Messmer, B.T., Bunke, H.: A New Algorithm for Error-Tolerant Subgraph Isomorphism Detection. IEEE Transactions on PAMI 20(5), 493–504 (1998)

    Google Scholar 

  5. Kuner, P.: Efficient Techniques to Solve the Subgraph Isomorphism Problem for Pattern Recognition in Line Images. In: Proceedings of 4th Scandinavian Conference on Image Analysis, Trondheim, Norway, pp. 333–340 (1985)

    Google Scholar 

  6. Habacha, A.H.: Structural Recognition of Disturbed Symbols Using Discrete Relaxation. In: Proceedings of 1st International Conference on Document Analysis and Recognition, Saint-Malo, France, vol. 1, pp. 170–178 (1991)

    Google Scholar 

  7. Wilson, R.C., Hancock, E.R.: Structural Matching by Discrete Relaxation. PAMI 19(6), 634–648 (1997)

    Google Scholar 

  8. Christmas, W.J., Kittler, J., Petrou, M.: Structural Matching in Computer Vision Using Probabilistic Relaxation. IEEE Transactions on PAMI 17(8), 749–764 (1995)

    Google Scholar 

  9. Kasturi, R., Bow, S., ELMasri, W., Shah, J., Gattiker, J., Mokate, U.: A system for interpretation of line drawings. IEEE Trans. on Pattern Analysis and Machine Intelligence 12(10) (1990)

    Google Scholar 

  10. Cordella, L.P., Vento, M.: Symbol recognition in documents: a collection of techniques? International Journal on Document Analysis and Recognition 3(2), 73–88 (2000)

    Article  Google Scholar 

  11. Messmer, B.T., Bunke, H.: Automatic learning and recognition of graphical symbols in engineering drawings. Graphics Recognition: Methods and Applications, 123–134 (1996)

    Google Scholar 

  12. Tombre, K., Dori, D.: Interpretation of engineering drawings. Handbook of Character Recognition and Document Image Analysis, 457–484 (1997)

    Google Scholar 

  13. Song, S., Lyu, M.R., Cai, S.: Effective multiresolution arc segmentation: Algorithms and performance evaluation. IEEE Trans. on Pattern Analysis and Machine Intelligence 16(11), 1491–1506 (2004)

    Article  Google Scholar 

  14. Wenyin, L., Zhai, J., Dori, D.: Extended summary of the arc segmentation contest. In: Blostein, D., Kwon, Y.-B. (eds.) GREC 2001. LNCS, vol. 2390, pp. 343–349. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  15. Rosin, P., West, G.: Segmentation of edges into lines and arcs. Image and Vision Computing 7(2), 109–114 (1989)

    Article  Google Scholar 

  16. Elliman, D.: An algorithm for arc segmentation in engineering drawings. In: Blostein, D., Kwon, Y.B. (eds.) GREC 2001. LNCS, vol. 2390, pp. 350–358. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  17. Conker, R.: A dual plane variation of the hough transform for detecting nonconcentric circles of different radius. Computer Vision and Image Processing 43, 115–132 (1988)

    Article  Google Scholar 

  18. Leavers, V.: The dynamic generalized hough transform: Its relationship to the probabilistic hough transforms and an application to the concurrent detection of circles and ellipses. Computer Vision, Graphics, Image Understanding 56(3), 381–398 (1992)

    MATH  Google Scholar 

  19. Dori, D.: Vector-based arc segmentation in the machine drawing understanding system environment. IEEE Trans. Pattern Analysis and Machine Intelligence 17(11), 1057–1068 (1995)

    Article  Google Scholar 

  20. Liu, W., Dori, D.: Incremental arc segmentation algorithm and its evaluation. IEEE Trans. Pattern Analysis and Machine Intelligence 20(4), 424–431 (1998)

    Article  Google Scholar 

  21. Blostein, D., Kwon, Y.-B. (eds.): GREC 2001. LNCS, vol. 2390. Springer, Heidelberg (2002), http://www.cs.cityu.edu.hk/liuwy/ArcContest/ArcSegContest.htm

    MATH  Google Scholar 

  22. Lladós, J., Kwon, Y.-B. (eds.): GREC 2003. LNCS, vol. 3088. Springer, Heidelberg (2004), http://www.cvc.uab.hk.es/grec03/contest.htm

    Google Scholar 

  23. Hilaire, X.: Ranvec and the arc segmentation contest: Second presentation. In: Liu, W., Llados, J. (eds.) Postproceedings of GREC 2005. LNCS. Springer, Heidelberg (to appear, 2006)

    Google Scholar 

  24. Khoo, K., Suganthan, P.: Evaluation of genetic operators and solution representations for shape recognition by genetic algorithm. Pattern Recognition Letters 23(13), 1589–1597 (2002)

    Article  MATH  Google Scholar 

  25. Sanniti di Baja, G.: Well-Shaped, Stable, and Reversible Skeletons from the (3,4)-Distance Transform. Journal of Visual Communication and Image Representation 5(1), 107–115 (1994)

    Article  Google Scholar 

  26. Li, S.Z.: Matching: Invariant to Translations, Rotations and Scale Changes. Pattern Recognition 25(6), 583–594 (1992)

    Article  MathSciNet  Google Scholar 

  27. Ambler, A.P., Barrow, H.G., Brown, C.M., Burstall, R.M., Popplestone, R.J.: A versatile computer-controlled assembly system. In: International Joint Conference on Artificial Intelligence, vol. 1, pp. 298–307 (1973)

    Google Scholar 

  28. Cross, A.D.J., Hancock, E.R.: Inexact Graph Matching with Genetic Search. In: Perner, P., Wang, P., Rosenfeld, A. (eds.) SSPR 1996. LNCS, vol. 1121, pp. 150–159. Springer, Heidelberg (1996)

    Google Scholar 

  29. Wang, Y.K., Fan, K.C., Horng, J.T.: Genetic-based search for error-correcting graph isomorphism. IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics 27(4) (1997)

    Google Scholar 

  30. Sammoud, O., Sorlin, S., Solnon, C., Ghédira, K.: A comparative study of ant colony optimization and reactive search for graph matching problems. In: European Conference on Evolutionary Computation in Combinatorial Optimization, pp. 317–326 (2006)

    Google Scholar 

  31. Qureshi, R.J., Ramel, J.Y., Cardot, H.: Graph based shapes representation and recognition. In: Escolano, F., Vento, M. (eds.) GbRPR. LNCS, vol. 4538, pp. 49–60. Springer, Heidelberg (2007)

    Google Scholar 

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Authors and Affiliations

  1. LORIA-UMR 7503, INPL, BP 239, 54506, Vandoeuvre-lès-Nancy, France

    J. -P. Salmon & L. Wendling

Authors
  1. J. -P. Salmon
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  2. L. Wendling
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Editor information

Wenyin Liu Josep Lladós Jean-Marc Ogier

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Salmon, J.P., Wendling, L. (2008). ARG Based on Arcs and Segments to Improve the Symbol Recognition by Genetic Algorithm. In: Liu, W., Lladós, J., Ogier, JM. (eds) Graphics Recognition. Recent Advances and New Opportunities. GREC 2007. Lecture Notes in Computer Science, vol 5046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88188-9_9

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  • DOI: https://doi.org/10.1007/978-3-540-88188-9_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88184-1

  • Online ISBN: 978-3-540-88188-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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