Search Results for author:
Found 13 papers, 5 papers with code
Interpretable statistical representations of neural population dynamics and geometry
The dynamics of neuron populations during many behavioural tasks evolve on low-dimensional manifolds.
Moment evolution equations and moment matching for stochastic image EPDiff
Models of stochastic image deformation allow study of time-continuous stochastic effects transforming images by deforming the image domain.
A Unifying and Canonical Description of Measure-Preserving Diffusions
A complete recipe of measure-preserving diffusions in Euclidean space was recently derived unifying several MCMC algorithms into a single framework.
Diffusion bridges for stochastic Hamiltonian systems and shape evolutions
Stochastically evolving geometric systems are studied in shape analysis and computational anatomy for modelling random evolutions of human organ shapes.
Numerical Analysis Computational Engineering, Finance, and Science Numerical Analysis Computational Physics
Semi-supervised classification on graphs using explicit diffusion dynamics
Classification tasks based on feature vectors can be significantly improved by including within deep learning a graph that summarises pairwise relationships between the samples.
Selective metamorphosis for growth modelling with applications to landmarks
We present a framework for shape matching in computational anatomy allowing users control of the degree to which the matching is diffeomorphic.
Stochastic Image Deformation in Frequency Domain and Parameter Estimation using Moment Evolutions
We apply a stochastic generalisation of the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework to model differences in the evolution of anatomical objects detected in populations of image data.
String Methods for Stochastic Image and Shape Matching
Matching of images and analysis of shape differences is traditionally pursued by energy minimization of paths of deformations acting to match the shape objects.
Differential geometry and stochastic dynamics with deep learning numerics
In this paper, we demonstrate how deterministic and stochastic dynamics on manifolds, as well as differential geometric constructions can be implemented concisely and efficiently using modern computational frameworks that mix symbolic expressions with efficient numerical computations.
Computational Geometry Computation 53A35, 53C17, 53C44, 70H05, 22E30 G.3; G.4; G.1.4
Stochastic metamorphosis with template uncertainties
In this paper, we investigate two stochastic perturbations of the metamorphosis equations of image analysis, in the geometrical context of the Euler-Poincar\'e theory.
Bridge Simulation and Metric Estimation on Landmark Manifolds
We present an inference algorithm and connected Monte Carlo based estimation procedures for metric estimation from landmark configurations distributed according to the transition distribution of a Riemannian Brownian motion arising from the Large Deformation Diffeomorphic Metric Mapping (LDDMM) metric.
A Geometric Framework for Stochastic Shape Analysis
We introduce a stochastic model of diffeomorphisms, whose action on a variety of data types descends to stochastic evolution of shapes, images and landmarks.
A Stochastic Large Deformation Model for Computational Anatomy
In the study of shapes of human organs using computational anatomy, variations are found to arise from inter-subject anatomical differences, disease-specific effects, and measurement noise.
