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We address the problem of segmenting high angular resolution diffusion images of the brain into cerebral regions corresponding to distinct white matter fiber bundles. We cast this problem as a manifold clustering problem in which distinct fiber bundles correspond to different submanifolds of the space of orientation distribution functions (ODFs). Our approach integrates tools from sparse representation...
In this paper, we propose distributed algorithms for estimating the average pose of an object viewed by a localized network of camera motes. To this effect, we propose distributed averaging consensus algorithms on the group of 3D rigid-body transformations, SE(3). We rigorously analyze the convergence of the proposed algorithms, and show that naive generalizations of Euclidean consensus algorithms...
We propose a novel algorithm for clustering data sampled from multiple submanifolds of a Riemannian manifold. First, we learn a representation of the data using generalizations of local nonlinear dimensionality reduction algorithms from Euclidean to Riemannian spaces. Such generalizations exploit geometric properties of the Riemannian space, particularly its Riemannian metric. Then, assuming that...
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