T
Tegan Emerson
Researcher at United States Naval Research Laboratory
Publications - 36
Citations - 578
Tegan Emerson is an academic researcher from United States Naval Research Laboratory. The author has contributed to research in topics: Computer science & Topological data analysis. The author has an hindex of 7, co-authored 26 publications receiving 388 citations. Previous affiliations of Tegan Emerson include Colorado State University & Pacific Northwest National Laboratory.
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Journal Article
Persistence images: a stable vector representation of persistent homology
Henry Adams,Tegan Emerson,Michael Kirby,Rachel Neville,Chris Peterson,Patrick D. Shipman,Sofya Chepushtanova,Eric Hanson,Francis C. Motta,Lori Ziegelmeier +9 more
TL;DR: In this article, a persistence diagram (PD) is converted to a finite-dimensional vector representation which is called a persistence image (PI) and proved the stability of this transformation with respect to small perturbations in the inputs.
Posted Content
Persistence Images: A Stable Vector Representation of Persistent Homology
Henry Adams,Sofya Chepushtanova,Tegan Emerson,Eric M. Hanson,Michael Kirby,Francis C. Motta,Rachel Neville,Chris Peterson,Patrick D. Shipman,Lori Ziegelmeier +9 more
TL;DR: This work converts a PD to a finite-dimensional vector representation which it is called a persistence image, and proves the stability of this transformation with respect to small perturbations in the inputs.
Posted Content
Topological Data Analysis of Task-Based fMRI Data from Experiments on Schizophrenia
TL;DR: This paper uses persistent homology to analyze networks that are constructed from task-based fMRI data from schizophrenia patients, healthy controls, and healthy siblings of schizophrenia patients and finds that the members of the sibling cohort have topological features that are distinct from the other two cohorts.
Posted Content
Persistence Images: An Alternative Persistent Homology Representation.
Sofya Chepushtanova,Tegan Emerson,Eric M. Hanson,Michael Kirby,Francis C. Motta,Rachel Neville,Chris Peterson,Patrick D. Shipman,Lori Ziegelmeier +8 more
TL;DR: It is shown that several machine learning techniques, applied to persistence images for classification tasks, yield high accuracy rates on multiple data sets and these sameMachine learning techniques fare better when applied to persistency images than when applied when it comes to persistence diagrams.
Journal ArticleDOI
Transport-based model for turbulence-corrupted imagery.
Jonathan M. Nichols,Tegan Emerson,Liam Cattell,S. Park,Andrey V. Kanaev,Frank Bucholtz,Abbie T. Watnik,Timothy Doster,Gustavo K. Rohde +8 more
TL;DR: A new model for turbulence-corrupted imagery is proposed based on the theory of optimal mass transport, and combining it with a least action principle, which suggests a new class of methods for approximately recovering the solution of the photon density flow created by a turbulent atmosphere.