A Belief Function Model for Pixel Data

Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 164)

Abstract

Image data i.e. pixel values are notably corrupted with uncertainty. A pixel value can be seen as uncertain because of additional noise due to acquisition conditions or compression. It is possible to represent a pixel value in a more imprecise but less uncertain way by considering it as interval-valued instead of a single-valued. The Belief Function Theory (BFT) allows to handle such interval-based pixel representations. We provide in this paper a model describing how to define belief functions from image data. The consistency of this model is demonstrated on edge detection experiments as conflicting pixel-based belief functions lead to image transitions detection.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bloch, I.: Some aspects of dempster-shafer evidence theory for classification of multi-modality medical images taking partial volume effect into account. Pattern Recognition Letters 17(8), 905–919 (1996)CrossRefGoogle Scholar
  2. 2.
    Canny, J.: Finding edges and lines in images. Tech. rep., Cambridge, MA, USA (1983)Google Scholar
  3. 3.
    Dempster, A.: A generalization of bayesian inference. Journal of Royal Statistical Society B 30, 205–247 (1968)MathSciNetGoogle Scholar
  4. 4.
    Klein, J., Colot, O.: Singular sources mining using evidential conflict analysis. International Journal of Approximate Reasoning 52, 1433–1451 (2011)CrossRefGoogle Scholar
  5. 5.
    Marr, D., Hildreth, E.: Theory of edge detection. Proceedings of the Royal Society of London, Series B, Biological Science 207(1167), 187–217 (1980)CrossRefGoogle Scholar
  6. 6.
    Prewitt, J.: Object enhancement and extraction. Picture Processing and Psychopictorics, pp. 75–149. Academic Press, New York (1970)Google Scholar
  7. 7.
    Roberts, L.: Machine perception of 3-D solids. Optical and Electro-optical Information Processing. MIT Press (1965)Google Scholar
  8. 8.
    Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)MATHGoogle Scholar
  9. 9.
    Sobel, I.: Camera model and machine perception. Ph.D. thesis, Stanford University (1970)Google Scholar
  10. 10.
    Vannoorenberghe, P., Macaire, L., Colot, O.: Evidence-based pixel labeling for color image segmentation. In: Computer Vision Research Progress, ch. 11, pp. 279–296. Nova Science (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.LAGIS FRE CNRS 3303Lille1 UniversityLilleFrance

Personalised recommendations