Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Scene appearance model based on spatial prediction

  • Special Issue Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

The appearance of a static scene as sensed by a camera changes considerably as a result of changes in the illumination that falls upon it. Scene appearance modeling is thus necessary for understanding which changes in the appearance of a scene are the result of illumination changes. For any camera, the appearance of the scene is a function of the illumination sources in the scene, the three-dimensional configuration of the objects in the scene and the reflectance properties of all the surfaces in the scene. A scene appearance model is described here as a function of the behavior of static illumination sources, within or beyond the scene, and arbitrary three-dimensional configurations of patches and their reflectance distributions. Based on the suggested model, a spatial prediction technique was developed to predict the appearance of the scene, given a few measurements within it. The scene appearance model and the prediction technique were developed analytically and tested empirically. Two potential applications are briefly explored.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24

Similar content being viewed by others

References

  1. Yuille, A., Snow, D., Epstein, R., Belhumeur, P.: Determining generative models of objects under varying illumination: shape and albedo from multiple images using SVD and integrability. Int. J. Comput. Vis. 35(3), 203–222 (1999)

    Article  Google Scholar 

  2. Belhumeur, P., Kriegman, D.: What is the set of images of an object under all possible lighting conditions? In: IEEE Conference Computer Vision and, Pattern Recognition, pp. 270–277 (1996)

  3. Epstein, R., Hallinan, P., Yuille, A.: \(5+-2\) Eigenimages suffice: an empirical investigation of low-dimensional lighting models. In: Proceedings of IEEE Workshop Physics-Based Vision, pp. 108–116 (1995)

  4. Hallinan, P.: A low-dimensional representation of human faces for arbitrary lighting conditions. In: Proceedings of IEEE Conference Computer Vision and, Pattern Recognition, pp. 995–999 (1994)

  5. Ronen, Basri, David, Jacobs: Lambertian reflectance and linear subspaces. IEEE Trans. Pattern Anal. Mach. Intell. 25(2), 218–233 (2003)

    Article  Google Scholar 

  6. Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 681–685 (2001)

    Article  Google Scholar 

  7. Prados, E., Faugeras, O.: Shape from Shading. In: Paragios, N. et al. (ed.) Handbook of Mathematical Models in Computer Vision. pp. 375–388. Springer, Berlin (2006)

  8. Horn, B.K.P.: Robot Vision. McGraw-Hill, NY (1986)

  9. Radke, R.J., Andra, S., Al-Kofahi, O., Roysam, B.: Image change detection algorithms: a systematic survey. IEEE Trans. Image Process. 14(3), 294–307 (2005)

    Article  MathSciNet  Google Scholar 

  10. Weiming, H., Xi, L., Xiaoqin, Z., Xinchu, S., Stephen, M., Zhongfei, Z.: Incremental tensor subspace learning and its applications to foreground segmentation and tracking. Int. J. Comput. Vis. 91(3), 303–327 (2011)

    Google Scholar 

  11. Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Proceedings of IEEE Conference on Computer Vision and, Pattern Recognition, vol. 2, pp. 246–252 (1999)

  12. Bhat, D.N., Nayar, S.K.: Ordinal measures for image correspondence. IEEE PAMI 20(4), 415–423 (1998)

    Article  Google Scholar 

  13. Parameswaran, V., Singh, M., Ramesh, V.: Illumination compensation based change detection using order consistency. In: Computer Vision and Pattern Recognition (CVPR) (2010)

  14. Cucchiara R., Grana C., Piccardi M., Prati A.: Detecting moving objects, ghosts and shadows in video streams. IEEE Trans. Pattern Anal. Mach. Intell. 25(10), 1337–1342 (2003)

    Google Scholar 

  15. Oliver, N.M., Rosario, B., Pentland, A.P.: A Bayesian computer vision system for modeling human interactions. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 831–843 (2000)

    Article  Google Scholar 

  16. Knuth, Donald E.: The Art of Computer Programming, Volume 2: Seminumerical Algorithms, 3rd edn. Addison-Wesley, Boston (1998)

  17. Lalonde, J.-F., Efros, A.A., Narasimhan, S.G.: Estimating natural illumination from a single outdoor image. In: International Conference on Computer Vision (2009)

  18. Kumar, R., Barmpoutis, A., Banerjee, A., Vemuri, B.C.: Face Relighting for Recognition. CISE REP-2008-451 (2008)

  19. Guyon, C.: Thierry Bouwmans and El-hadi Zahzah Robust Principal Component Analysis for Background Subtraction: Systematic Evaluation and Comparative Analysis Chapter 12 “Principal Component Analysis”. In: INTECH, pp. 223–237 (2012)

  20. Skifstad, K., Jain, R.: Illumination independent change detection for real world image sequences. Comput. Vis. Graph. Image Process. 46(3), 387–399 (1989)

    Article  Google Scholar 

  21. Hsu, Y.Z., Nagel, H.H., Rekers, G.: New likelihood test methods for change detection in image sequences. Comput. Vis. Graph. Image Process. 26(1), 73–106 (1984)

    Article  Google Scholar 

  22. Improved linear dependence and vector model for illumination invariant change detection. Proc. IEEE. 89(10), 1368–1381 (2001)

    Google Scholar 

  23. Goyette, N., Jodoin, P.-M., Porikli, F., Konrad, J., Ishwar, P.: changedetection.net: a new change detection benchmark dataset. In: Proceedings of IEEE Workshop on Change Detection (CDW2) at CVPR12, Providence, RI, 16–21 June (2012)

  24. Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: principles and practice of background maintenance. In: Seventh International Conference on Computer Vision, September 1999, Kerkyra, Greece, pp. 255–261, IEEE Computer Society Press (1999)

Download references

Author information

Authors and Affiliations

  1. Beer Sheva, Israel

    Rami R. Hagege

Authors
  1. Rami R. Hagege
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rami R. Hagege.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hagege, R.R. Scene appearance model based on spatial prediction. Machine Vision and Applications 25, 1241–1256 (2014). https://doi.org/10.1007/s00138-013-0565-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-013-0565-2

Keywords

Search

Navigation