Feature Detection and Hypothesis Testing for Extremely Noisy Nanoparticle Images using Topological Data Analysis

Cites Work

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Confidence sets for persistence diagrams
Hypothesis testing for topological data analysis
Morse theory for filtrations and efficient computation of persistent homology
Robust statistics, hypothesis testing, and confidence intervals for persistent homology on metric measure spaces
The tight constant in the Dvoretzky-Kiefer-Wolfowitz inequality
Stability of persistence diagrams
Lipschitz functions have \(L_{p}\)-stable persistence
An entropy-based persistence barcode
Topological fidelity and image thresholding: a persistent homology approach
Topological persistence and simplification
Measures of order for nearly hexagonal lattices
Functional summaries of persistence diagrams
Extending hypothesis testing with persistent homology to three or more groups
Efficient Computation of Persistent Homology for Cubical Data
Probability for Statistics and Machine Learning
The Accumulated Persistence Function, a New Useful Functional Summary Statistic for Topological Data Analysis, With a View to Brain Artery Trees and Spatial Point Process Applications
Maxima of discretely sampled random fields, with an application to 'bubbles'
Geometric and Topological Inference
DTM-Based Filtrations
Topological Data Analysis with Applications
Global Envelope Tests for Spatial Processes
Statistical Analysis and Modelling of Spatial Point Patterns
A density-based approach to feature detection in persistence diagrams for firn data
Multiple hypothesis testing with persistent homology

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