Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing

ISMM 2015: Mathematical Morphology and Its Applications to Signal and Image Processing pp 289–300Cite as

Intelligent Object Detection Using Trees

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9082))

Abstract

In this paper a method is proposed for detection and localisation of objects in images using connected operators. Existing methods typically use a moving window to detect objects, which means that an image needs to be scanned at each pixel location for each possible scale and orientation of the object of interest which makes such methods computationally expensive. Some of those methods have made some improvements in computational efficiency but they still rely on a moving window. Use of connected operators for efficiently detecting objects has typically been limited to objects consisting of a single connected region (either based on simple or more generalized connectivities). The proposed method uses component trees to efficiently detect and locate objects in an image. These objects can consist of many segments that are not necessarily connected. The computational efficiency of the connected operators is maintained as objects of interest of all scales and orientations are detected using two component trees constructed from the input image without using any moving window.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ali, K., Fleuret, F., Hasler, D., Fua, P.: A real-time deformable detector. IEEE Trans. Patt. Anal. Mach. Intell. 34(2), 225–239 (2012)

    Article  Google Scholar 

  2. Batman, S., Dougherty, E.R.: Size distributions for multivariate morphological granulometries: texture classification and statistical properties. Optical Engineering 36, 1518–1529 (1997)

    Article  Google Scholar 

  3. Breen, E.J., Jones, R.: Attribute openings, thinnings and granulometries. Comp. Vis. Image Understand. 64, 377–389 (1996)

    Article  Google Scholar 

  4. Carlinet, E., Geraud, T.: A comparative review of component tree computation algorithms. IEEE Trans. Image Proc. 23(9), 3885–3895 (2014)

    Article  MathSciNet  Google Scholar 

  5. Chen, Q., Song, Z., Dong, J., Huang, Z., Hua, Y., Yan, S.: Contextualizing object detection and classification. IEEE Trans. Patt. Anal. Mach. Intell. 37(1), 13–27 (2015)

    Article  Google Scholar 

  6. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  7. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Addison-Wesley (2002)

    Google Scholar 

  8. Heijmans, H.J.A.M.: Connected morphological operators for binary images. Comput. Vis. Image Understand. 73, 99–120 (1999)

    Article  MATH  Google Scholar 

  9. Najman, L., Couprie, M.: Building the component tree in quasi-linear time. IEEE Trans. Image Proc. 15(11), 3531–3539 (2006)

    Article  Google Scholar 

  10. Ouzounis, G.K., Wilkinson, M.H.F.: Mask-based second generation connectivity and attribute filters. IEEE Trans. Patt. Anal. Mach. Intell. 29(2), 990–1004 (2007)

    Article  Google Scholar 

  11. Salembier, P., Oliveras, A., Garrido, L.: Anti-extensive connected operators for image and sequence processing. IEEE Trans. Image Proc. 7, 555–570 (1998)

    Article  Google Scholar 

  12. Sofou, A., Tzafestas, C., Maragos, P.: Segmentation of soilsection images using connected operators. In: Int. Conf. Image Proc. 2001, pp. 1087–1090 (2001)

    Google Scholar 

  13. Soille, P.: Morphological Image Analysis: Principles and Applications, 2nd edn. Springer, New York (2002)

    Google Scholar 

  14. Szeliski, R.: Computer Vision: Algorithms and Applications, 1st edn. Springer-Verlag New York, Inc., New York (2010)

    Google Scholar 

  15. Urbach, E.R., Roerdink, J.B.T.M., Wilkinson, M.H.F.: Connected shape-size pattern spectra for rotation and scale-invariant classification of gray-scale images. IEEE Trans. Patt. Anal. Mach. Intell. 29(2), 272–285 (2007)

    Article  Google Scholar 

  16. Urbach, E.R., Stepinski, T.F.: Automatic detection of sub-km craters in high resolution planetary images. Planetary and Space Science 57, 880–887 (2009)

    Article  Google Scholar 

  17. Urbach, E.R.: Contextual image filtering. In: 24th International Conference Image and Vision Computing New Zealand, pp. 299–303 (November 2009)

    Google Scholar 

  18. Urbach, E.R., Pervukhina, M., Bischof, L.: Segmentation of cracks in shale rock. In: Soille, P., Pesaresi, M., Ouzounis, G.K. (eds.) ISMM 2011. LNCS, vol. 6671, pp. 451–460. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  19. Viola, P., Jones, M.J.: Robust real-time face detection. International Journal of Computer Vision 57(2), 137–154 (2004)

    Article  Google Scholar 

  20. Westenberg, M.A., Roerdink, J.B.T.M., Wilkinson, M.H.F.: Volumetric attribute filtering and interactive visualization using the max-tree representation. IEEE Trans. Image Proc. 16(12), 2943–2952 (2007)

    Article  MathSciNet  Google Scholar 

  21. Xu, Y., Geraud, T., Najman, L.: Context-based energy estimator: Application to object segmentation on the tree of shapes. In: Proc. Int. Conf. Image Proc. 2012, pp. 1577–1580 (September 2012)

    Google Scholar 

  22. Yang, B., Flusser, J., Suk, T.: 3d rotation invariants of gaussian-hermite moments. Pattern Recogn. Lett. 54, 18–26 (2015)

    Article  Google Scholar 

  23. Zimmermann, K., Hurych, D., Svoboda, T.: Non-rigid object detection with local interleaved sequential alignment (lisa). IEEE Trans. Patt. Anal. Mach. Intell. 36(4), 731–743 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Florence, Florence, Italy

    Erik R. Urbach

Authors
  1. Erik R. Urbach
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of Iceland, Reykjavik, Iceland

    Jón Atli Benediktsson

  2. Université de Grenoble Alpes, Grenoble, France

    Jocelyn Chanussot

  3. ESIEE Paris, Noisy-le-Grand Cedex, France

    Laurent Najman

  4. ESIEE Paris, Noisy-le-Grand Cedex, France

    Hugues Talbot

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Urbach, E.R. (2015). Intelligent Object Detection Using Trees. In: Benediktsson, J., Chanussot, J., Najman, L., Talbot, H. (eds) Mathematical Morphology and Its Applications to Signal and Image Processing. ISMM 2015. Lecture Notes in Computer Science(), vol 9082. Springer, Cham. https://doi.org/10.1007/978-3-319-18720-4_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-18720-4_25

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18719-8

  • Online ISBN: 978-3-319-18720-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation