Advertisement

Ultimate Leveling Based on Mumford-Shah Energy Functional Applied to Plant Detection

  • Charles Gobber
  • Wonder A. L. Alves
  • Ronaldo F. Hashimoto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)

Abstract

This paper presents a filter based on energy functions applied to the ultimate levelings which are powerful image operators based on numerical residues. Within a multi-scale framework, these operators analyze a given image under a series of levelings. Thus, contrasted objects can be detected if a relevant residue is generated when they are filtered out by one of these levelings. During the residual extraction process, it is very common that undesirable regions of the input image contain residual information that should be filtered out. These undesirable residual regions often include desirable residual regions due to the design of the ultimate levelings which consider maximum residues. In this paper, we improve the residual information by filtering out residues extracted from undesirable regions. In order to test our approach, some experiments were conducted in plant dataset and the results show the robustness of our approach.

Keywords

Ultimate levelings Morphological trees Mumford-Shah energy functional Plant detection 

References

  1. 1.
    Alves, W.A., Hashimoto, R.F., Marcotegui, B.: Ultimate levelings, computer vision and image understandingGoogle Scholar
  2. 2.
    Li, W., Haese-Coat, V., Ronsin, J.: Residues of morphological filtering by reconstruction for texture classification. Pattern Recogn. 30(7), 1081–1093 (1997)CrossRefGoogle Scholar
  3. 3.
    Pesaresi, M., Benediktsson, J.: A new approach for the morphological segmentation of high-resolution satellite imagery. IEEE Trans. Geosci. Remote Sens. 39(2), 309–320 (2001)CrossRefGoogle Scholar
  4. 4.
    Retornaz, T., Marcotegui, B.: Scene text localization based on the ultimate opening. In: Proceedings of the 8th International Symposium on Mathematical Morphology and its Applications to Image and Signal Processing, vol. 1, pp. 177–188 (2007)Google Scholar
  5. 5.
    Dalla Mura, M., Benediktsson, J.A., Waske, B., Bruzzone, L.: Morphological attribute profiles for the analysis of very high resolution images. IEEE Trans. Geosci. Remote Sens. 48(10), 3747–3762 (2010)CrossRefGoogle Scholar
  6. 6.
    Hernández, J., Marcotegui, B.: Shape ultimate attribute opening. Image Vis. Comput. 29(8), 533–545 (2011)CrossRefGoogle Scholar
  7. 7.
    Alves, W.A.L., Hashimoto, R.F.: Ultimate grain filter. In: IEEE International Conference on Image Processing, Paris, France, pp. 2953–2957 (2014)Google Scholar
  8. 8.
    Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems. Commun. Pure Appl. Math. 42(5), 577–685 (1989)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Xu, Y., Géraud, T., Najman, L.: Hierarchical image simplification and segmentation based on Mumford-Shah-salient level line selection. Pattern Recogn. Lett. Part 3 83, 278–286 (2016)CrossRefGoogle Scholar
  10. 10.
    Alves, W.A.L., Morimitsu, A., Hashimoto, R.F.: Scale-space representation based on levelings through hierarchies of level sets. In: Benediktsson, J.A., Chanussot, J., Najman, L., Talbot, H. (eds.) ISMM 2015. LNCS, vol. 9082, pp. 265–276. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-18720-4_23 CrossRefGoogle Scholar
  11. 11.
    Caselles, V., Monasse, P.: Geometric Description of Images as Topographic Maps, Berlin. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-04611-7 CrossRefMATHGoogle Scholar
  12. 12.
    Alves, W., Morimitsu, A., Castro, J., Hashimoto, R.: Extraction of numerical residues in families of levelings. In: 2013 26th SIBGRAPI - Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 349–356 (2013)Google Scholar
  13. 13.
    Beucher, S.: Numerical residues. Image Vis. Comput. 25(4), 405–415 (2007)CrossRefGoogle Scholar
  14. 14.
    Fabrizio, J., Marcotegui, B.: Fast implementation of the ultimate opening. In: Proceedings of the 9th International Symposium on Mathematical Morphology, pp. 272–281 (2009)Google Scholar
  15. 15.
    Ballester, C., Caselles, V., Igual, L., Garrido, L.: Level lines selection with variational models for segmentation and encoding. J. Math. Imaging Vis. 27(1), 5–27 (2007)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Minervini, M., Fischbach, A., Scharr, H., Tsaftaris, S.A.: Finely-grained annotated datasets for image-based plant phenotyping. Pattern Recogn. Lett. 81, 80–89 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Charles Gobber
    • 1
  • Wonder A. L. Alves
    • 1
    • 2
  • Ronaldo F. Hashimoto
    • 2
  1. 1.Informatics and Knowledge Management Graduate ProgramUniversidade Nove de JulhoSão PauloBrazil
  2. 2.Department of Computer Science, Institute of Mathematics and StatisticsUniversidade de São PauloSão PauloBrazil

Personalised recommendations