Skip to main content

Detecting Scene Elements Using Maximally Stable Colour Regions

  • Conference paper

Part of the Communications in Computer and Information Science book series (CCIS,volume 82)

Abstract

Image processing for autonomous robots is nowadays very popular. In our paper, we show a method how to extract information from a camera attached on a robot to acquire locations of targets the robot is looking for. We apply maximally stable colour regions (a method originally used for image matching) to obtain an initial set of candidate regions. This set is then filtered using application specific filters to find only the regions that correspond to scene elements of interest. The presented method has been applied in practice and performs well even under varying illumination conditions since it does not rely heavily on manually specified colour thresholds. Furthermore, no colour calibration is needed.

Keywords

  • Autonomous robot
  • Maximally Stable Colour Regions

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-642-16370-8_10
  • Chapter length: 9 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   84.99
Price excludes VAT (USA)
  • ISBN: 978-3-642-16370-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   109.00
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: Proceedings of the British Machine Vision Conference 2002 (BMVC’02), pp. 384–393 (2002)

    Google Scholar 

  2. Forssén, P.-E.: Maximally stable colour regions for recognition and matching. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR’07 (2007)

    Google Scholar 

  3. Matas, J., Obdrzalek, S., Chum, O.: Local affine frames for wide-baseline stereo. In: Proceedings of the 16th International Conference on Pattern Recognition, ICPR’02 (2002)

    Google Scholar 

  4. Forssén, P.E., Lowe, D.: Shape descriptors for maximally stable extremal regions. In: IEEE International Conference on Computer Vision, ICCV’07 (2007)

    Google Scholar 

  5. Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2004) ISBN: 0521540518

    CrossRef  MATH  Google Scholar 

  6. Douglas, D., Peucker, T.: Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Canadian Cartographer 10, 112–122 (1973)

    CrossRef  Google Scholar 

  7. Eurobot: Eurobot autonomous robot contest (2009), http://www.eurobot.org

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Obdržálek, D., Basovník, S., Mach, L., Mikulík, A. (2010). Detecting Scene Elements Using Maximally Stable Colour Regions. In: Gottscheber, A., Obdržálek, D., Schmidt, C. (eds) Research and Education in Robotics - EUROBOT 2009. EUROBOT 2009. Communications in Computer and Information Science, vol 82. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16370-8_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-16370-8_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16369-2

  • Online ISBN: 978-3-642-16370-8

  • eBook Packages: Computer ScienceComputer Science (R0)