Skip to main content

A Linear Approximation Based Method for Noise-Robust and Illumination-Invariant Image Change Detection

  • Conference paper
Book cover Advances in Multimedia Information Processing - PCM 2004 (PCM 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3333))

Included in the following conference series:

Abstract

Image change detection plays a very important role in real-time video surveillance systems. To deal with the illumination, a category of linear algebra based algorithms were designed in the literature. They have been proved to be effective for surveillance environment with lighting and shadowing. In practice, other than illumination, the detecting process is also influenced by the noises of cameras and reflections. In this paper, analysis is made systemically on the existing linear algebra detectors, showing their intrinsic weakness in case of noises. In order to get less sensitive to noises, a novel method is proposed based on the technique of linear approximation. Theoretical and experimental analysis both show its robustness and high performance for noisy image change detection.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aach, T., Kaup, A.: Statistical model-based change detection in moving video. Signal Processing 31, 165–180 (1993)

    Article  MATH  Google Scholar 

  2. Aach, T., Kaup, A., Mester, R.: Change detection in image sequences using Gibbs random fields. In: Proc. IEEE, Workshop on Intelligent Signal Processing and Communication Systems, Sendai, Japan, October 1993, pp. 56–61 (1993)

    Google Scholar 

  3. Aach, T., Kaup, A.: Bayesian algorithms for adaptive change detection in image sequences using Markov random fields. Signal Processing: Image Communication 7, 147–160 (1995)

    Article  Google Scholar 

  4. Hsu, Y.Z., Nagel, H.H., Reckers, G.: New likelihood test methods for change detection in image sequences. Computer Vision, Graphics, and Image Processing 26, 73–106 (1984)

    Article  Google Scholar 

  5. Skifstad, K., Jain, R.: Illumination independent change detection for real world image sequences. Computer Vision, Graphics, and Image Processing 46, 387–399 (1989)

    Article  Google Scholar 

  6. Durucan, E., Ebrahimi, T.: Robust and illumination invariant change detection based on linear dependence. In: Proc. of 10th European Signal Processing Conference, Tampere, Finland, September 2000, pp. 1141–1144 (2000)

    Google Scholar 

  7. Durucan, E., Ebrahimi, T.: Change detection and background extraction by linear algebra. Proc. IEEE 89(10), 1368–1381 (2001)

    Article  Google Scholar 

  8. Phong, B.T.: Illumination for computer generated pictures. Commun. ACM 18, 311–317 (1975)

    Article  Google Scholar 

  9. Richard, L., Burden, J.: Douglas Faires: Numerical Analysis. Brooks Cole (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Gao, B., Liu, TY., Cheng, QS., Ma, WY. (2004). A Linear Approximation Based Method for Noise-Robust and Illumination-Invariant Image Change Detection. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds) Advances in Multimedia Information Processing - PCM 2004. PCM 2004. Lecture Notes in Computer Science, vol 3333. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30543-9_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30543-9_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23985-7

  • Online ISBN: 978-3-540-30543-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics