We demonstrate our mouse MR image and shape analysis pipeline. The long term goal of our work is the description of structural shape variations in normal, genetically identical mice and the subsequent detection of pathological phenotypes in genetically modified mice. The pipeline begins with the acquisition and reconstruction of high resolution MR images of mice. Regions of the images containin...

This paper discusses a trainable system to extract features for image segmentation based on non-linear mapping of local features. Supervised training methods are presented, for artificial neural diabolo networks, which produce a mapping comparable to Fisher’s linear discriminant mapping. This mapping can be used to decrease dimensionality whilst preserving class separability. It is shown that t...

This thesis explores applications of vector field processing to shape deformations. We present a novel method to construct divergence-free vector fields which are used to deform shapes by vector field integration (Chapter 2). The resulting deformation is volume-preserving and no self-intersections occur. We add more controllability to this approach by introducing implicit boundaries (Chapter 3)...

We introduce a new stopping-time argument, adapted to handle linear sums of noncompactly-supported functions that satisfy fairly weak decay, smoothness, and cancellation conditions. We use the argument to obtain a new Littlewood-Paley-type result for such sums. 0. Introduction. First, an apology. The title, though correct, is somewhat misleading. It should be “Global almostorthogonality implies...

Existing mapped WENO schemes can hardly prevent spurious oscillations while preserving high resolutions at long output times. We reveal in this paper the essential reason for such phenomena. It is actually caused by that mapping function these not preserve order of nonlinear weights stencils. The may be increased non-smooth stencils and decreased smooth then indicated to require set functions O...

We consider the graph whose vertex set is a conjugacy class C consisting of finite-rank self-adjoint operators on complex Hilbert space H. The dimension H assumed to be not less than 3. In case when from have two eigenvalues only, we obtain Grassmann formed by k-dimensional subspaces H, where k smallest eigenspaces. Chow's theorem describes automorphisms this for k>1. Under assumption that more...