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Found 27 papers, 6 papers with code
Rescuing referral failures during automated diagnosis of domain-shifted medical images
Here, we address a fundamental challenge with selective classification during automated diagnosis with domain-shifted medical images.
Shape-Graph Matching Network (SGM-net): Registration for Statistical Shape Analysis
This, in turn, requires optimization over the permutation group, made challenging by differences in nodes (in terms of numbers, locations) and edges (in terms of shapes, placements, and sizes) across objects.
Learning Pose Image Manifolds Using Geometry-Preserving GANs and Elasticae
This paper investigates the challenge of learning image manifolds, specifically pose manifolds, of 3D objects using limited training data.
Data-Driven, Soft Alignment of Functional Data Using Shapes and Landmarks
Alignment or registration of functions is a fundamental problem in statistical analysis of functions and shapes.
Elastic Shape Analysis of Tree-like 3D Objects using Extended SRVF Representation
We demonstrate the utility of this framework in comparing, matching, and computing geodesics between biological objects such as neurons and botanical trees.
Rethinking Multidimensional Discriminator Output for Generative Adversarial Networks
The study of multidimensional discriminator (critic) output for Generative Adversarial Networks has been underexplored in the literature.
Geo-FARM: Geodesic Factor Regression Model for Misaligned Pre-Shape Responses in Statistical Shape Analysis
The problem of using covariates to predict shapes of objects in a regression setting is important in many fields.
Elastic Shape Analysis of Brain Structures for Predictive Modeling of PTSD
Predictive modeling with such interactions is of paramount interest in heterogeneous mental disorders such as PTSD, where trauma exposure interacts with brain shape changes to influence behavior.
Shape Analysis of Functional Data with Elastic Partial Matching
In this case, it is more natural to model such data with sliding boundaries and use partial matching, i. e., only a part of a function is matched to another function.
4D Atlas: Statistical Analysis of the Spatiotemporal Variability in Longitudinal 3D Shape Data
With this formulation, the statistical analysis of 4D surfaces can be cast as the problem of analyzing trajectories embedded in a nonlinear Riemannian manifold.
Data Science for Motion and Time Analysis with Modern Motion Sensor Data
Unsolved technical questions include: How the motion and time information can be extracted from the motion sensor data, how work motions and execution rates are statistically modeled and compared, and what are the statistical correlations of motions to the rates?
Statistical Shape Analysis of Brain Arterial Networks (BAN)
This framework is then used to generate shape summaries of BANs from 92 subjects, and to study the effects of age and gender on shapes of BAN components.
Representations, Metrics and Statistics For Shape Analysis of Elastic Graphs
This paper utilizes a quotient structure to develop efficient algorithms for computing these quantities, leading to useful statistical tools, including principal component analysis and analytical statistical testing and modeling of graphical shapes.
A Quotient Space Formulation for Generative Statistical Analysis of Graphical Data
Complex analyses involving multiple, dependent random quantities often lead to graphical models - a set of nodes denoting variables of interest, and corresponding edges denoting statistical interactions between nodes.
Discovering Common Change-Point Patterns in Functional Connectivity Across Subjects
This paper studies change-points in human brain functional connectivity (FC) and seeks patterns that are common across multiple subjects under identical external stimulus.
Analyzing Dynamical Brain Functional Connectivity As Trajectories on Space of Covariance Matrices
Human brain functional connectivity (FC) is often measured as the similarity of functional MRI responses across brain regions when a brain is either resting or performing a task.
Numerical Inversion of SRNF Maps for Elastic Shape Analysis of Genus-Zero Surfaces
Recent developments in elastic shape analysis (ESA) are motivated by the fact that it provides comprehensive frameworks for simultaneous registration, deformation, and comparison of shapes.
Elastic Functional Coding of Riemannian Trajectories
We propose to learn an embedding such that each action trajectory is mapped to a single point in a low-dimensional Euclidean space, and the trajectories that differ only in temporal rates map to the same point.
Temporally Coherent Interpretations for Long Videos Using Pattern Theory
Graph-theoretical methods have successfully provided semantic and structural interpretations of images and videos.
Elastic Functional Coding of Human Actions: From Vector-Fields to Latent Variables
Learning an accurate low dimensional embedding for actions could have a huge impact in the areas of efficient search and retrieval, visualization, learning, and recognition.
Bayesian Clustering of Shapes of Curves
The elastic-inner product matrix obtained from the data is modeled using a Wishart distribution whose parameters are assigned carefully chosen prior distributions to allow for automatic inference on the number of clusters.
Video-Based Action Recognition Using Rate-Invariant Analysis of Covariance Trajectories
Using a natural Riemannain metric on vector bundles of SPDMs, we compute geodesic paths and geodesic distances between trajectories in the quotient space of this vector bundle, with respect to the re-parameterization group.
Bayesian Active Contours with Affine-Invariant, Elastic Shape Prior
Active contour, especially in conjunction with prior-shape models, has become an important tool in image segmentation.
Rate-Invariant Analysis of Trajectories on Riemannian Manifolds with Application in Visual Speech Recognition
We apply this framework to the problem of speech recognition using both audio and visual components.
Generative Models for Functional Data using Phase and Amplitude Separation
This paper presents an approach that relies on separating the phase (x-axis) and amplitude (y-axis), then modeling these components using joint distributions.
Computation Statistics Theory Statistics Theory 62F99
Signal Estimation Under Random Time-Warpings and Nonlinear Signal Alignment
First, we derive an estimator for the equivalence class of the unknown signal using the notion of Karcher mean on the quotient space of equivalence classes.
Registration of Functional Data Using Fisher-Rao Metric
We introduce a novel geometric framework for separating the phase and the amplitude variability in functional data of the type frequently studied in growth curve analysis.
Statistics Theory Applications Methodology Statistics Theory
