A Belief Function Model for Pixel Data

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.