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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Canny, J.: Finding edges and lines in images. Tech. rep., Cambridge, MA, USA (1983)
Dempster, A.: A generalization of bayesian inference. Journal of Royal Statistical Society B 30, 205–247 (1968)
Klein, J., Colot, O.: Singular sources mining using evidential conflict analysis. International Journal of Approximate Reasoning 52, 1433–1451 (2011)
Marr, D., Hildreth, E.: Theory of edge detection. Proceedings of the Royal Society of London, Series B, Biological Science 207(1167), 187–217 (1980)
Prewitt, J.: Object enhancement and extraction. Picture Processing and Psychopictorics, pp. 75–149. Academic Press, New York (1970)
Roberts, L.: Machine perception of 3-D solids. Optical and Electro-optical Information Processing. MIT Press (1965)
Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)
Sobel, I.: Camera model and machine perception. Ph.D. thesis, Stanford University (1970)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Klein, J., Colot, O. (2012). A Belief Function Model for Pixel Data. In: Denoeux, T., Masson, MH. (eds) Belief Functions: Theory and Applications. Advances in Intelligent and Soft Computing, vol 164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29461-7_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-29461-7_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29460-0
Online ISBN: 978-3-642-29461-7
eBook Packages: EngineeringEngineering (R0)