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.
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© 2012 Springer-Verlag Berlin Heidelberg
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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
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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
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