Optical Graph Recognition

  • Christopher Auer
  • Christian Bachmaier
  • Franz J. Brandenburg
  • Andreas Gleißner
  • Josef Reislhuber
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7704)

Abstract

Optical graph recognition (OGR) reverses graph drawing. A drawing transforms the topological structure of a graph into a graphical representation. Primarily, it maps vertices to points and displays them by icons and it maps edges to Jordan curves connecting the endpoints. OGR transforms the digital image of a drawn graph into its topological structure. It consists of four phases, preprocessing, segmentation, topology recognition, and postprocessing. OGR is based on established digital image processing techniques. Its novelty is the topology recognition where the edges are recognized with emphasis on the attachment to their vertices and on edge crossings.

Our prototypical implementation OGRup shows the effectiveness of the approach and produces a GraphML file which can be used for further algorithmic studies and graph drawing tools.

References

  1. 1.
    Bachmaier, C., Brandenburg, F.J., Forster, M., Holleis, P., Raitner, M.: Gravisto: Graph Visualization Toolkit. In: Pach, J. (ed.) GD 2004. LNCS, vol. 3383, pp. 502–503. Springer, Heidelberg (2005), http://gravisto.fim.uni-passau.de/ CrossRefGoogle Scholar
  2. 2.
    Borgefors, G.: Distance transformations in digital images. Computer Vision, Graphics and Image Processing 34(3), 344–371 (1986)CrossRefGoogle Scholar
  3. 3.
    Bunke, H., Wang, P.S.: Handbook of Character Recognition and Document Image Analysis. World Scientific (1997)Google Scholar
  4. 4.
    Castleman, K.R.: Digital Image Processing. Prentice-Hall (1996)Google Scholar
  5. 5.
    Cauchie, J., Fioletb, V., Villers, D.: Optimization of an Hough transform algorithm for the search of a center. Pattern Recogn. 41, 567–574 (2008)MATHCrossRefGoogle Scholar
  6. 6.
    Chow, C.K., Kaneko, T.: Automatic boundary detection of the left ventricle from cineangiograms. Comput. Biomed. Res. 5(4), 388–410 (1972)CrossRefGoogle Scholar
  7. 7.
    Condurache, A.P., Aach, T.: Vessel segmentation in angiograms using hysteresis thresholding. In: IAPR Conference on Machine Vision Applications 2005, pp. 269–272 (2005)Google Scholar
  8. 8.
    Davies, E.R.: A modified Hough scheme for general circle location. Pattern Recogn. Lett. 7(1), 37–43 (1988)CrossRefGoogle Scholar
  9. 9.
    Davies, E.R.: Machine Vision: Theory, Algorithms, Practicalities, 2nd edn. Academic Press (1997)Google Scholar
  10. 10.
    Di Battista, G., Eades, P., Tamassia, R., Tollis, I.G.: Graph Drawing: Algorithms for the Visualization of Graphs. Prentice Hall (1999)Google Scholar
  11. 11.
    Didimo, W., Eades, P., Liotta, G.: Drawing graphs with right angle crossings. Theor. Comput. Sci. 412(39), 5156–5166 (2011)MathSciNetMATHCrossRefGoogle Scholar
  12. 12.
    Duda, R.O., Hart, P.E.: Use of the Hough transformation to detect lines and curves in pictures. Commun. ACM 15(1), 11–15 (1972)CrossRefGoogle Scholar
  13. 13.
    Estrada, R., Tomasi, C.: Manuscript bleed-through removal via hysteresis thresholding. In: Proc. International Conference on Document Analysis and Recognition, ICDAR 2009. IEEE (2009)Google Scholar
  14. 14.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice-Hall (2002)Google Scholar
  15. 15.
    Huang, W., Eades, P., Hong, S.H.: Beyond time and error: A cognitive approach to the evaluation of graph drawings. In: Proc. Beyond Time and Errors: Novel Evaluation Methods for Information Visualization, BELIV 2008, pp. 3:1–3:8. ACM (2008)Google Scholar
  16. 16.
    Huang, W., Eades, P., Hong, S.H.: Measuring effectiveness of graph visualizations: A cognitive load perspective. Inform. Visual. 8(3), 139–152 (2009)CrossRefGoogle Scholar
  17. 17.
    Huang, W., Hong, S.H., Eades, P.: Effects of crossing angles. In: Fujishiro, I., Li, H., Ma, K.L. (eds.) Proc. IEEE Pacific Visualization Symposium, PacificVis 2008, pp. 41–46. IEEE (2008)Google Scholar
  18. 18.
    Lauren, V., Pisinger, G.: Automated analysis of vessel diameters in MR images. Visualization, Imaging, and Image Processing, 931–936 (2004)Google Scholar
  19. 19.
    Pach, J.: Every Graph Admits an Unambiguous Bold Drawing. In: van Kreveld, M., Speckmann, B. (eds.) GD 2011. LNCS, vol. 7034, pp. 332–342. Springer, Heidelberg (2012)Google Scholar
  20. 20.
    Reislhuber, J.: Graph Recognition. Master’s thesis, University of Passau (2011), http://www.infosun.fim.uni-passau.de/br/publications/mt-reislhuber-2011.pdf
  21. 21.
    Ringel, G.: Ein Sechsfarbenproblem auf der Kugel. Abhandlungen aus dem Mathematischen Seminar der Universität Hamburg 292, 107–117 (1965)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Christopher Auer
    • 1
  • Christian Bachmaier
    • 1
  • Franz J. Brandenburg
    • 1
  • Andreas Gleißner
    • 1
  • Josef Reislhuber
    • 1
  1. 1.University of PassauPassauGermany

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