We present BaRe-ESA, a novel Riemannian framework for human body scan representation, interpolation and extrapolation.
This paper introduces a set of numerical methods for Riemannian shape analysis of 3D surfaces within the setting of invariant (elastic) second-order Sobolev metrics.
Motivated by applications from computer vision to bioinformatics, the field of shape analysis deals with problems where one wants to analyze geometric objects, such as curves, while ignoring actions that preserve their shape, such as translations, rotations, or reparametrizations.
Finally, the predictor uses the original spectrum and the modified F0 contour to generate a corresponding target spectrum.
Square root normal fields (SRNF) considerably simplify the computation of certain elastic distances between parametrized surfaces.
This paper introduces a new mathematical formulation and numerical approach for the computation of distances and geodesics between immersed planar curves.
We study in detail the construction of kernel metrics on varifold spaces and the resulting topological properties of those metrics, then propose a mathematical model for diffeomorphic registration of varifolds under a specific group action which we formulate in the framework of optimal control theory.
Optimization and Control
In the classical setting, signals are represented as vectors and the dictionary learning problem is posed as a matrix factorization problem where the data matrix is approximately factorized into a dictionary matrix and a sparse matrix of coefficients.
This paper proposes a new framework and algorithms to address the problem of diffeomorphic registration on a general class of geometric objects that can be described as discrete distributions of local direction vectors.
Optimization and Control Classical Analysis and ODEs 49M25, 49Q20, 49J15
Advanced diffusion magnetic resonance imaging (dMRI) techniques, like diffusion spectrum imaging (DSI) and high angular resolution diffusion imaging (HARDI), remain underutilized compared to diffusion tensor imaging because the scan times needed to produce accurate estimations of fiber orientation are significantly longer.
This paper introduces a general setting for the construction of data fidelity metrics between oriented or non-oriented geometric shapes like curves, curve sets or surfaces.
High angular resolution diffusion imaging (HARDI) can produce better estimates of fiber orientation than the popularly used diffusion tensor imaging, but the high number of samples needed to estimate diffusivity requires longer patient scan times.
no code implementations • 1 Dec 2016 • Kwame S. Kutten, Nicolas Charon, Michael I. Miller, J. T. Ratnanather, Jordan Matelsky, Alexander D. Baden, Kunal Lillaney, Karl Deisseroth, Li Ye, Joshua T. Vogelstein
Due to the novelty of this microscopy technique it is impractical to use absolute intensity values to align these images to existing standard atlases.
In this paper, we describe in detail a model of geometric-functional variability between fshapes.
Optimization and Control Differential Geometry 49M25, 49Q20, 58B32, 58E50, 68U05, 68U10
Therefore, we developed a method (Mask-LDDMM) for registering CLARITY images, that automatically find the brain boundary and learns the optimal deformation between the brain and atlas masks.
This article introduces a full mathematical and numerical framework for treating functional shapes (or fshapes) following the landmarks of shape spaces and shape analysis.
More specifically, problems occur with structures like acute pikes because of canceling effects of currents or with data that consists in many disconnected pieces like fiber bundles for which currents require a consistent orientation of all pieces.