Directional Structures Detection Based on Morphological Line-Segment and Orientation Functions

  • Iván R. Terol-Villalobos
  • Luis A. Morales-Hernández
  • Gilberto Herrera-Ruiz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4992)


In the present paper a morphological approach for segmenting directional structures is proposed. This approach is based on the concept of the line-segment and orientation functions. The line-segment function is computed from the supremum of directional erosions. This function contains the sizes of the longest lines that can be included in the structure. To determine the directions of the line segments, the orientation function which contains the angles of the line segments it is built when the line-segment function is computed. Next, by combining both functions, a weighted partition is built using the watershed transformation. Finally, the elements of the partition are merged according to some directional and size criteria for computing the desired segmentation of the image using a RAG structure.


Line Segment Orientation Function Mathematical Morphology Directional Structure Catchment Basin 
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  1. 1.
    Soille, P., Talbot, H.: Directional morphological filtering. Trans. on Pattern Anal. Machine Intell. 23(11), 1313–1329 (2001)CrossRefGoogle Scholar
  2. 2.
    Soille, P., Breen, E.J., Jones, R.: Recursive implementation of erosions and dilations along discrete lines at arbitrary angles. IEEE Trans. on Pattern Anal. Machine Intell. 18(5), 562–567 (1996)CrossRefGoogle Scholar
  3. 3.
    Jeulin, D., Kurdy, M.: Directional mathematical morphology for oriented image restoration and segmentation. Acta Stereologica 11, 545–550 (1992)Google Scholar
  4. 4.
    Tuzikov, A., Soille, P., Jeulin, D., Vermeulen, P.: Extraction of grid patterns on stamped metal sheets using mathematical morphology. In: Proc. of International Conference on Pattern Recognition, vol. 1, pp. 425–428 (1992)Google Scholar
  5. 5.
    Oliveira, M.A., Leite, N.J.: Reconnection of fingerprint ridges based on morphological operators and multiscale directional information. In: Proc. of XVII Brazilian Symposium on Computer Graphics and Image Processing, pp. 122–129 (2004)Google Scholar
  6. 6.
    Morales-Hernández, L.A., Terol-Villalobos, I.R., Dominguez-González, A., Herrera-Ruiz, G.: Characterization of fingerprints using two new directional morphological approaches. In: Advances in Dynamics, Instrumentation and Control, pp. 325–334. World Scientific Publishing Co, Singapore (2007)Google Scholar
  7. 7.
    Cappelli, R., Lumini, A.: Fingerprint classification by directional image partitioning. IEEE Trans. on Pattern Anal. Machine Intell. 21(5), 402–421 (1999)CrossRefGoogle Scholar
  8. 8.
    Park, C.H., Lee, J.J., Smith, M.J.T., Park, K.H.: Singular point detection by shape analysis of directional fields in fingerprints. Pattern Recognition 39, 839–855 (2006)zbMATHCrossRefGoogle Scholar
  9. 9.
    Li, J., Yau, W.Y., Wang, H.: Constrained nonlinear models of fingerprint orientations with prediction. Pattern Recognition 39, 102–114 (2006)CrossRefGoogle Scholar
  10. 10.
    Lee, J.K., Newman, T.S., Gary, G.A.: Oriented connectivity-based method for segmenting solar loops. Pattern Recognition 39, 246–259 (2006)CrossRefGoogle Scholar
  11. 11.
    Bahlmann, C.: Directional features in online handwriting recognition. Pattern Recognition 39, 115–125 (2006)CrossRefGoogle Scholar
  12. 12.
    Kass, M., Witkin, A.: Analyzing oriented pattern. Computer Vision, Graphics, and Image Processing 37(3), 362–385 (1987)CrossRefGoogle Scholar
  13. 13.
    Bazen, A.M., Gerez, S.H.: Systematic methods for the computation of the directional fields and singular points of fingerprints. IEEE-Trans. on Pattern Analysis and Machine Intelligence 24(7), 905–919 (2002)CrossRefGoogle Scholar
  14. 14.
    Serra, J.: Image Analysis and Mathematical Morphology. Theoretical advances, vol. II. Academic Press, London (1988)Google Scholar
  15. 15.
    Heijmans, H.J.A.M.: Morphological Image Operators. Academic Press, New York (1994)zbMATHGoogle Scholar
  16. 16.
    Soille, P.: Morphological Image Analysis: Principles and Applications, 2nd edn. Springer, Heidelberg, Berlin (2003)zbMATHGoogle Scholar
  17. 17.
    Meyer, F., Beucher, S.: Morphological segmentation. J. Vis. Comm. Image Represent 1, 21–46 (1990)CrossRefGoogle Scholar
  18. 18.
    Crespo, J., Schafer, R., Serra, J., Meyer, F., Gratin, C.: A flat zone approach: A general low-level region merging segmentation method. Signal Process 62, 37–60 (1997)zbMATHCrossRefGoogle Scholar
  19. 19.
    Salembier, Ph., Serra, J.: Morphological multiscale image segmentation. In: Proc. SPIE-Visual Communications and Image Processing, vol. 1818, pp. 620–631 (1882)Google Scholar
  20. 20.
    Shafarenko, L., Petrou, M., Kittler, J.: Automatic watershed segmentation of randomly textured color images. IEEE Trans. on Image Processing 6(11), 1530–1544 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Iván R. Terol-Villalobos
    • 1
  • Luis A. Morales-Hernández
    • 2
  • Gilberto Herrera-Ruiz
    • 2
  1. 1.CIDETEQ,S.C., Parque Tecnológico Querétaro S/N, SanFandila-Pedro EscobedoQuerétaroMexico
  2. 2.Facultad en IngenieríaUniversidad Autónoma de QuerétaroMéxico

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