Skip to main content
SpringerLink
Log in
Book cover

International Conference on Intelligent Data Engineering and Automated Learning

IDEAL 2012: Intelligent Data Engineering and Automated Learning - IDEAL 2012 pp 360–365Cite as

Comparative Study of Methods for Segmentation of Digital Images of Birds

  • Conference paper

  • 1550 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7435))

Abstract

The observation of birds is important not only for academic reasons, but also for the monitoring of preservation areas. An important step for developing a program for automatic recognition of birds is the segmentation of the image, that is, highlighting the bird from the background in a digital image. This paper aims to present a comparative study using methods of segmentation in digital images of birds. In this work, a method based on cellular automata is compared with other method based on active contours.

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. Birdwatcher, http://www.birdwatcher.com.br

  2. Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comput. Vis. 22(1), 61–79 (1997)

    Article  MATH  Google Scholar 

  3. Chan, T., Vese, L.: Active contours without edges. IEEE Trans. on Image Processing 10(2), 266–277 (2001)

    Article  MATH  Google Scholar 

  4. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification, 2nd edn. Wiley, New York (2001)

    MATH  Google Scholar 

  5. Ermentrout, G.B., Edelstein-Keshet, L.: Cellular automata approaches to Biological modeling. J. Theor. Biol. 160, 97–113 (1993)

    Article  Google Scholar 

  6. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. Int. J. of Computer Vision 1(4), 321–331 (1988)

    Article  Google Scholar 

  7. Lankton, S.: Sparse Field Methods. Technical Report (2009)

    Google Scholar 

  8. Lankton, S., Tannenbaum, A.: Localizing Region-Based Active Contours. IEEE Trans. on Image Processing 17(11), 2029–2039 (2008)

    Article  MathSciNet  Google Scholar 

  9. Matlab Central, http://www.mathworks.com/matlabcentral/fileexchange/19091-growcut-image-segmentation

  10. Osher, S., Sethian, J.A.: Front Propagating with Curvature Dependent Speed: Algorithms Based on Hamilton-Jacobi Formulations. Journal Computational Physics 78, 12–49 (1988)

    Article  MathSciNet  Google Scholar 

  11. Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Elsevier (2009)

    Google Scholar 

  12. U.S. Fish & Wildlife Service, http://library.fws.gov/pubs/birding_natsurvey06.pdf

  13. Vezhnevets, V., Konouchine, V.: ’GrowCut’ – Interactive Multi-Label N-D Image Segmentation by Cellular Automata. In: Graphicon 2005, Russia (2005)

    Google Scholar 

  14. Whitaker, R.: A level-set approach to 3D reconstruction from range data. Int. J. Comput. Vis. 29(3), 203–231 (1998)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Escola de Engenharia Elétrica e da Computação, Universidade Federal de Goiás (UFG), Caixa Postal 131, 74001-970, Goiânia, GO, Brasil

    Felipe de Sousa Nobre, Paulo César Miranda Machado & Rodrigo Pinto Lemos

Authors
  1. Felipe de Sousa Nobre
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paulo César Miranda Machado
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rodrigo Pinto Lemos
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, The University of Manchester, M13 9PL, Manchester, UK

    Hujun Yin

  2. Department of Electrical Engineering, Federal University of Rio Grande do Norte, Lagoa Nova, 59072-970, Natal, RN, Brazil

    José A. F. Costa

  3. Department of Teleinformatics Engineering, Federal University of Ceará, Campus of Pici, CP 6005, 60455-760, Fortaleza, CE, Brazil

    Guilherme Barreto

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

de Sousa Nobre, F., Machado, P.C.M., Lemos, R.P. (2012). Comparative Study of Methods for Segmentation of Digital Images of Birds. In: Yin, H., Costa, J.A.F., Barreto, G. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2012. IDEAL 2012. Lecture Notes in Computer Science, vol 7435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32639-4_44

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-32639-4_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32638-7

  • Online ISBN: 978-3-642-32639-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation