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Matrix-valued kernels for shape deformation analysis
TLDR
A systematic study and classification of non-scalar kernels for Reproducing Kernel Hilbert Spaces (RKHS), to be used in the analysis of deformation in shape spaces endowed with metrics induced by the action of groups of diffeomorphisms.
Vision based navigation for an unmanned aerial vehicle
TLDR
A hierarchical approach to path planning is used for autonomous navigation of unmanned aerial vehicles (UAVs) based on computer vision, which distinguishes between a global offline computation based on a coarse known model of the environment and a local online computation, based on the information coming from the vision system.
Random Sampling of a Continuous-time Stochastic Dynamical System: Analysis, State Estimation, and Applications
TLDR
A lower bound on the sampling rate is found that makes it possible to keep the estimation error variance below a given threshold with an arbitrary probability.
Sobolev metrics on diffeomorphism groups and the derived geometry of spaces of submanifolds
Given a finite-dimensional manifold , the group of diffeomorphisms diffeomorphism of  which decrease suitably rapidly to the identity, acts on the manifold of submanifolds of  of diffeomorphism-type
Sectional Curvature in Terms of the Cometric, with Applications to the Riemannian Manifolds of Landmarks
TLDR
This paper fully explore the case of geodesics on which only two points have nonzero momenta and compute the sectional curvatures of 2-planes spanned by the tangents to such geodesic, and gives insight into the geometry of the full manifolds of landmarks.
The differential geometry of landmark shape manifolds: metrics, geodesics, and curvature
The study of shapes and their similarities is central in computer vision, in that it allows to recognize and classify objects from their representation. One has the interest of defining a distance
A Linear Systems Approach to Imaging Through Turbulence
TLDR
This paper introduces a statistical dynamic model for the generation of turbulence based on linear dynamical systems (LDS) and expands the model to include the unknown image as part of the unobserved state and applies Kalman filtering to estimate such state.
Implementation of the Centroid Method for the Correction of Turbulence
TLDR
The centroid method for the correction of turbulence consists in computing the Karcher-Fr echet mean of the sequence of input images to produce useful results from an arbitrarily small set ofinput images.
The Centroid Method for Imaging through Turbulence
A simple and effective method for imaging through ground-level atmospheric turbulence.
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