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We present an extension to the Diffusion Basis Function Model for fitting the in vivo brain axonal orientations from Diffusion Weighted Magnetic Resonance Images. The standard Diffusion Basis Functions method assumes that the observed Magnetic Resonance signal at each voxel is a linear combination of a static set of basis functions with equally distributed orientations into the 3D unitary sphere....
We present a framework for image segmentation based on quadratic programming, i.e., by minimization of a quadratic regularized energy linearly constrained. In particular, we present a new variational derivation of the quadratic Markov measure field (QMMF) models, which can be understood as a procedure for regularizing model preferences (memberships or likelihoods). We also present efficient optimization...
The estimation of principal diffusion directions in brain white matter has been extensively studied by processing Diffusion Weighted Magnetic Resonance Images. Those studies present competitive results for both, a single diffusion orientation, as well as for multiple diffusion orientations at voxels where the axon fibers cross or split. However, in the best of our knowledge, all the available methods...
We present a method for foreground-background video segmentation in real-time that may be used in applications as, for instance, Background Substitution, Analysis of Surveillance Cameras, Highway Cars Detection and so on. Our approach implements a probabilistic segmentation based on the binary Quadratic Markov Measure Fields models (QMMFs). That framework regularizes the likelihood of each pixel to...
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