Skip to main content
SpringerLink
Log in

A Color Constancy Algorithm Using Photodetector Characteristics of a Camera for Indoor Scenes

  • Conference paper
  • First Online:
Robot Intelligence Technology and Applications 4

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 447))

  • 2162 Accesses

Abstract

According to a recent research, illumination invariant features could be available to guarantee robust and reliable performance of vision systems. The research incorporated the imaging sensor properties to the vision algorithm, which is a general case in robotics contexts. However, these features are only applicable for the outdoor scenes. This paper develops an algorithm that enhances the features reported in the previous research. The features work for the indoor scenes with the proposed algorithm. Experiments are conducted to verify the performance of the proposed algorithm. It is verified that the features enhanced by the proposed algorithm become robust to illumination fluctuations.

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

Access this chapter

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

Institutional subscriptions

References

  1. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Computer VisionECCV 2006, pp. 404–417. Springer (2006)

    Google Scholar 

  2. Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: Computer VisionECCV 2010, pp. 778–792. Springer (2010)

    Google Scholar 

  3. Catalin, M.: Mobile phone camera possibilities for spectral imaging (Master’s thesis, University of Eastern Finland, Jeonsuu, Finland). Retrieved from http://epublications.uef.fi/pub/ (2014)

  4. Dissanayake, M.W.M.G., Newmau, P., Clark, S., Durrant-Whyte, H.F.: A solution to the simultaneous localization and map bnilding (SLAM) problem. Trans. IEEE Robot. Autom. 17(3) (2001)

    Google Scholar 

  5. Ebner, M.: Color Constancy, Wiley Series in Imaging Science and Technology. Wiley (2007)

    Google Scholar 

  6. Finlayson, G.D., Hordley, S.D.: Color constancy at a pixel. JOSA A 18(2), 253–264 (2001)

    Google Scholar 

  7. Finlayson, G.D., Hordley, S.D.: Color constancy at a pixel. J. Opt. Soc. Am. A 18, 253–264 (2001)

    Google Scholar 

  8. Gross, R., Brajovic, V.: An image preprocessing algorithm for illumination invariant face recognition. In: Proceedings of the AVBPA, pp. 10–18 (2003)

    Google Scholar 

  9. Henderson, S., Hodgkiss, D.: The spectral energy distribution of daylight. Br. J. Appl. Phys. 14(3), 125 (1963)

    Google Scholar 

  10. Hernandez-Andres, J., Romero, J., Nieves, J.L., Lee, R.L. Jr., et al.: Color and spectral analysis of daylight in Southern Europe. JOSA A 18(6), 1325–1335 (2001)

    Google Scholar 

  11. Horn, B.K.P.: Determining lightness from an image. Comput. Graph. Image Process. 3, 277–299 (1974)

    Google Scholar 

  12. Jedynak, B., Fleuret, F.: 3D Object recognition from geometric queries. In: Proceedings of the ImageCom 96, Bordeaux, France (1996)

    Google Scholar 

  13. Land, E.H., McCann, J.J.: Lightness and retinex theory. J. Opt. Soc. Am. 61, 1–11 (1971)

    Google Scholar 

  14. Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)

    Google Scholar 

  15. Maddern, W., Stewart, A.D., McManus, C., Upcroft, B., Churchill, W., Newman, P.: Illumination invariant imaging: applications in robust vision-based localisation, mapping and classification for autonomous vehicles. In: Proceedings of Workshop on Visual Place Recognition in Changing Environments, IEEE International Conference on Robotics and Automation (ICRA) (2014)

    Google Scholar 

  16. Ratnasingam, S., Collins, S., Hernandez-Andres, J.: Optimum sensors for color constancy in scenes illuminated by daylight. JOSA A 27(10), 2198–2207 (2010)

    Google Scholar 

  17. Ratnasingam, S., Collins, S.: Study of the photodetector characteristics of a camera for color constancy in natural scenes. JOSA A 27(2), 286–294 (2010)

    Google Scholar 

  18. Tuzel, O., Porikli, F., Meer, P.: Region covariance: a fast descriptor for detection and classification. In: Proceedings of the Ninth European Conference on Computer Vision (ECCV 06), vol. 2, pp. 589–600 (2006)

    Google Scholar 

Download references

Acknowledgments

This work was partly supported by Institute for Information& communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. B0101-15-0551, Virtual Creatures with Digital DNA) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2014R1A2A1A10051551).

Author information

Authors and Affiliations

  1. School of Electrical Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea

    Ue-Hwan Kim & Jong-Hwan Kim

Authors
  1. Ue-Hwan Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jong-Hwan Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ue-Hwan Kim .

Editor information

Editors and Affiliations

  1. Electrical Engineering, Korea Advanced Institute of Science & Technology, Daejeon, Korea (Republic of)

    Jong-Hwan Kim

  2. Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada

    Fakhri Karray

  3. School of Information and Communication Technology, Griffith University, Gold Coast, Queensland, Australia

    Jun Jo

  4. Center for Intelligent Technologies, Technical University of Kosice, Kosice, Slovakia

    Peter Sincak

  5. Civil and Environmental Engineering, Korea Advanced Institute of Science & Technology, Daejeon, Korea (Republic of)

    Hyun Myung

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing Switzerland

About this paper

Cite this paper

Kim, UH., Kim, JH. (2017). A Color Constancy Algorithm Using Photodetector Characteristics of a Camera for Indoor Scenes. In: Kim, JH., Karray, F., Jo, J., Sincak, P., Myung, H. (eds) Robot Intelligence Technology and Applications 4. Advances in Intelligent Systems and Computing, vol 447. Springer, Cham. https://doi.org/10.1007/978-3-319-31293-4_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-31293-4_41

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31291-0

  • Online ISBN: 978-3-319-31293-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation