Morphology Based Spatial Relationships between Local Primitives in Line Drawings

  • Naeem A. Bhatti
  • Allan Hanbury
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7042)


Local primitives and their spatial relationships are useful in the analysis, recognition and retrieval of document and patent binary images. In this paper, a morphology based approach is proposed to establish the connections between the local primitives found at the optimally detected junction points and end points. The grayscale geodesic dilation is employed as the basic technique by taking a marker image with gray values at the local primitives and the skeleton of the original image as the mask image. The geodesic paths along the skeleton between the local primitives are traversed and their points of contact are protected by updating the mask image after each geodesic dilation iteration. By scanning the final marker image for the contact points of the traversed geodesic paths, connections between the local primitives are established. The proposed approach is robust and scale invariant.


local primitives spatial relationships grayscale geodesic dilation 


  1. 1.
    Amores, J., Sebe, N., Radeva, P.: Context-based object-class recognition and retrieval by generalized correlograms. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 1818–1833 (2007)CrossRefGoogle Scholar
  2. 2.
    Bergevin, R., Filiatrault, A.: Enhancing Contour Primitives by Pairwise Grouping and Relaxation. In: Kamel, M.S., Campilho, A. (eds.) ICIAR 2007. LNCS, vol. 4633, pp. 222–233. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    Bhatti, N.A., Hanbury, A.: Co-occurrence bag of words for object recognition. In: Proceedings of the 15th Computer Vision Winter Workshop, pp. 21–28 (2010)Google Scholar
  4. 4.
    Bhatti, N.A., Hanbury, A.: Detection and classification of local primitives in line drawings. In: Proceedings of the 35th Austrian Association for Pattern Recognition (2011)Google Scholar
  5. 5.
    Bhatti, N.A., Hanbury, A.: Granulometry based detection of junction and end points in patent drawings. In: Proceedings of the 7th International Symposium on Image and Signal Processing and Analysis, ISPA (2011)Google Scholar
  6. 6.
    Desolneux, A., Moisan, L., Morel, J.-M.: Seeing, Thinking and Knowing. In: Carsetti, A. (ed.) Kluwer Academic Publishers, Dordrecht (2004)Google Scholar
  7. 7.
    Fonseca, M.J., Ferreira, A., Jorge, J.A.: Content-based retrieval of technical drawings. Special Issue of International Journal of Computer Applications in Technology, IJCAT (2004)Google Scholar
  8. 8.
    Fonseca, M.J., Ferreira, A., Jorge, J.A.: Sketch-based retrieval of complex drawings using hierarchical topology and geometry. Computer Aided Design 41(12), 1067–1081 (2009)CrossRefGoogle Scholar
  9. 9.
    Förstner, W.: Uncertain neighborhood relations of point sets and fuzzy delaunay triangulation. In: Mustererkennung 1999, 21. DAGM-Symposium, pp. 213–222 (1999)Google Scholar
  10. 10.
    Heikkila, M., Pietikainen, M., Schmid, C.: Description of interest regions with local binary patterns. Pattern Recognition 42(3), 425–436 (2009)CrossRefzbMATHGoogle Scholar
  11. 11.
    Huet, B., Guarascio, G., Kern, N.J., Mérialdo, B.: Relational skeletons for retrieval in patent drawings. In: ICIP, pp. 737–740 (2001)Google Scholar
  12. 12.
    Leung, W.H., Chen, T.: User-independent retrieval of free-form hand-drawn sketches. In: Proc. of the IEEE ICASSP 2002, pp. 2029–2032. IEEE Press (2002)Google Scholar
  13. 13.
    Liu, R., Wang, Y., Baba, T., Masumoto, D.: Shape detection from line drawings with local neighborhood structure. Pattern Recognition 43(5), 1907–1916 (2010)CrossRefzbMATHGoogle Scholar
  14. 14.
    Park, J.H., Um, B.S.: A new approach to similarity retrieval of 2-d graphic objects based on dominant shapes. Pattern Recogn. Lett. 20(6), 591–616 (1999)CrossRefGoogle Scholar
  15. 15.
    Parker, C., Chen, T.: Hierarchical matching for retrieval of hand-drawn sketches. In: ICME, pp. 29–32. IEEE Computer Society, Washington, DC, USA (2003)Google Scholar
  16. 16.
    Santosh, K.C., Wendling, L., Lamiroy, B.: Unified Pairwise Spatial Relations: An Application to Graphical Symbol Retrieval. In: Ogier, J.-M., Liu, W., Lladós, J. (eds.) GREC 2009. LNCS, vol. 6020, pp. 163–174. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  17. 17.
    Soille, P.: Morphological Image Analysis: Principles and Applications, 2nd edn. Springer, Heidelberg (2003)zbMATHGoogle Scholar
  18. 18.
    Forstner, W., Heuel, S.: A dual, scalable and hierarchical representation for. perceptual organization of binary images. In: Workshop on Perceptual Organization in Computer Vision. IEEE Computer Society (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Naeem A. Bhatti
    • 1
  • Allan Hanbury
    • 2
  1. 1.Institute of Computer Aided AutomationVienna University of TechnologyViennaAustria
  2. 2.Institute of Software Technology and Interactive SystemsVienna University of TechnologyViennaAustria

Personalised recommendations