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Document is unavailable: This DOI was registered to an article that was not presented by the author(s) at this conference. As per section 8.2.1.B.13 of IEEE's "Publication Services and Products Board Operations Manual," IEEE has chosen to exclude this article from distribution. We regret any inconvenience.
This work deals with the use of a probabilistic quad-tree graph (Hidden Markov Tree, HMT) to provide fast computation, improved robustness and an effective interpretational framework for image analysis and processing in oncology. Thanks to two efficient aspects (multi observation and multi resolution) of HMT and Bayesian inference, we exploited joint statistical dependencies between hidden states...
Parametric modeling and estimation of non-Gaussian multidimensional probability density function is a difficult problem whose solution is required by many applications in signal and image processing. A lot of efforts have been devoted to escape the usual Gaussian assumption by developing perturbed Gaussian models such as spherically invariant random vectors (SIRVs). In this work, we introduce an alternative...
In this paper, we present an original generalization of the CFAR technique . The technique of CFAR consists in testing two alternative assumptions ldquopresence of targetrdquo versus ldquoabsence of targetrdquo in a distance-azimuth cell called ldquocell under testrdquo. In the case where the noise is Gaussian and additive, one can show that the CFAR is equivalent to withdraw from the signal under...
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