Advertisement

Incremental Attribute Computation in Component-Hypertrees

  • Alexandre MorimitsuEmail author
  • Wonder Alexandre Luz Alves
  • Dennis José da Silva
  • Charles Ferreira Gobber
  • Ronaldo Fumio HashimotoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11564)

Abstract

Component-hypertrees are structures that store nodes of multiple component trees built with increasing neighborhoods, meaning they retain the same desirable properties of component trees but also store nodes from multiple scales, at the cost of increasing time and memory consumption for building, storing and processing the structure. In recent years, algorithmic advances resulted in optimization for both building and storing hypertrees. In this paper, we intend to further extend advances in this field, by presenting algorithms for efficient attribute computation and statistical measures that analyze how attribute values vary when nodes are merged in bigger scales. To validate the efficiency of our method, we present complexity and time consumption analyses, as well as a simple application to show the usefulness of the statistical measurements.

Keywords

Component tree Component-hypertree Connected operators Attribute computation 

Notes

Acklowledgements

This study was financed in part by the CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Finance Code 001); FAPESP - Fundação de Amparo a Pesquisa do Estado de São Paulo (Proc. 2015/01587-0 and 2018/15652-7); CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico (Proc. 428720/2018-8).

References

  1. 1.
    Braga-Neto, U., Goutsias, J.: Connectivity on complete lattices: new results. Comput. Vis. Image Underst. 85(1), 22–53 (2002)CrossRefGoogle Scholar
  2. 2.
    Epshtein, B., Ofek, E., Wexler, Y.: Detecting text in natural scenes with stroke width transform. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2963–2970 (2010)Google Scholar
  3. 3.
    Gray, S.B.: Local properties of binary images in two dimensions. IEEE Trans. Comput. C–20(5), 551–561 (1971)CrossRefGoogle Scholar
  4. 4.
    Jones, R.: Connected filtering and segmentation using component trees. Comput. Vis. Image Underst. 75(3), 215–228 (1999)CrossRefGoogle Scholar
  5. 5.
    Karatzas, D., Mestre, S.R., Mas, J., Nourbakhsh, F., Roy, P.P.: ICDAR 2011 robust reading competition - challenge 1: reading text in born-digital images (web and email). In: 2011 International Conference on Document Analysis and Recognition, pp. 1485–1490 (2011)Google Scholar
  6. 6.
    Kiwanuka, F.N., Ouzounis, G.K., Wilkinson, M.H.F.: Surface-area-based attribute filtering in 3D. In: Wilkinson, M.H.F., Roerdink, J.B.T.M. (eds.) ISMM 2009. LNCS, vol. 5720, pp. 70–81. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-03613-2_7CrossRefGoogle Scholar
  7. 7.
    Morimitsu, A., Alves, W.A.L., Hashimoto, R.F.: Incremental and efficient computation of families of component trees. In: Benediktsson, J.A., Chanussot, J., Najman, L., Talbot, H. (eds.) ISMM 2015. LNCS, vol. 9082, pp. 681–692. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-18720-4_57CrossRefzbMATHGoogle Scholar
  8. 8.
    Morimitsu, A., Alves, W.A.L., Silva, D.J., Gobber, C.F., Hashimoto, R.F.: Minimal component-hypertrees. In: Couprie, M., Cousty, J., Kenmochi, Y., Mustafa, N. (eds.) DGCI 2019. LNCS, vol. 11414, pp. 276–287. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-14085-4_22CrossRefGoogle Scholar
  9. 9.
    Nayef, N., et al.: ICDAR 2017 robust reading challenge on multi-lingual scene text detection and script identification - RRC-MLT. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 01, pp. 1454–1459 (2017)Google Scholar
  10. 10.
    Neumann, L., Matas, J.: Real-time scene text localization and recognition. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3538–3545 (2012)Google Scholar
  11. 11.
    Ouzounis, G.K., Wilkinson, M.H.F.: Mask-based second-generation connectivity and attribute filters. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 990–1004 (2007)CrossRefGoogle Scholar
  12. 12.
    Passat, N., Naegel, B.: Component-hypertrees for image segmentation. In: Soille, P., Pesaresi, M., Ouzounis, G.K. (eds.) ISMM 2011. LNCS, vol. 6671, pp. 284–295. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-21569-8_25CrossRefzbMATHGoogle Scholar
  13. 13.
    Passat, N., Naegel, B., Rousseau, F., Koob, M., Dietemann, J.L.: Interactive segmentation based on component-trees. Pattern Recogn. 44(10), 2539–2554 (2011). semi-Supervised Learning for Visual Content Analysis and UnderstandingCrossRefGoogle Scholar
  14. 14.
    Retornaz, T., Marcotegui, B.: Scene text localization based on the ultimate opening. In: Proceedings of the 8 th International Symposium on Mathematical Morphology, October 10–13, 2007, MCT/INPE, vol. 1, pp. 177–188. Rio de Janeiro (2007)Google Scholar
  15. 15.
    Salembier, P., Oliveras, A., Garrido, L.: Antiextensive connected operators for image and sequence processing. IEEE Trans. Image Process. 7(4), 555–570 (1998)CrossRefGoogle Scholar
  16. 16.
    Serra, J.: Connectivity on complete lattices. J. Math. Imaging Vis. 9(3), 231–251 (1998)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Silva, D.J., Alves, W.A.L., Morimitsu, A., Hashimoto, R.F.: Efficient incremental computation of attributes based on locally countable patterns in component trees. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 3738–3742 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alexandre Morimitsu
    • 1
    Email author
  • Wonder Alexandre Luz Alves
    • 2
  • Dennis José da Silva
    • 1
  • Charles Ferreira Gobber
    • 2
  • Ronaldo Fumio Hashimoto
    • 1
    Email author
  1. 1.Instituto de Matemática e EstatísticaUniversidade de São PauloSão PauloBrazil
  2. 2.Universidade Nove de JulhoSão PauloBrazil

Personalised recommendations