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Neural Computation

Segmentation of multi-modal images

Assume two (or more) images of the same scene, but captured with different technologies (in the example below they are MRT- and PET- tomographies). A segmentation of these images is an interesting task itself even if all "other" parameters are known: parameters of the prior model, pixel correspondences etc. This is a nontrivial task if some segments are not distinguishable in one image at all. The key idea to overcome this difficulty is to segment the underlying scene rather then the images themselves. If, in addition, the images are not registered and furthermore some parameters of the prior model are unknown, then the task becomes really challenging.

In this work we propose a method for unifying registration and segmentation of multi-modal images assuming that the hidden scene model is a Gibbs probability distribution.

B. Flach, D. Schlesinger, E. Kask and A. Skulisch. Unifying Registration and Segmentation for Multi-sensor Images. L. Van Gool (Ed.): DAGM 2002, LNCS 2449, pp. 190-197, 2002.
pdf (302k)

Stand: 1.2.2010, 14:28 Uhr
Autor: Dipl.-Inf. Denis Kirmizigül