A Wasserstein-Type Distance for Gaussian Mixtures on Vector Bundles with Applications to Shape Analysis-Reference-Cited by-同舟云学术

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A Wasserstein-Type Distance for Gaussian Mixtures on Vector Bundles with Applications to Shape Analysis

Published:2024-07-11 Issue:3 Volume:17 Page:1433-1466
ISSN:1936-4954
Container-title:SIAM Journal on Imaging Sciences
language:en
Short-container-title:SIAM J. Imaging Sci.

Author:

Wilson Michael¹,Needham Tom²,Park Chiwoo³,Kundu Suparteek⁴,Srivastava Anuj¹

Affiliation:

1. Department of Statistics, Florida State University, Tallahassee, FL 32306 USA.

2. Department of Mathematics, Florida State University, Tallahassee, FL 32306 USA.

3. Department of Industrial and Systems Engineering, University of Washington, Seattle, WA 98195 USA.

4. The University of Texas MD Anderson Cancer Center, Houston, TX 77030 USA.

Funder

National Institutes of Health

Division of Civil, Mechanical and Manufacturing Innovation

National Science Foundation

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Reference40 articles.

1. L. Ambrosio and N. Gigli, A User’s Guide to Optimal Transport, Springer-Verlag, Berlin, 2013, pp. 1–155, https://doi.org/10.1007/978-3-642-32160-3_1.

2. Elastic Metrics on Spaces of Euclidean Curves: Theory and Algorithms

3. Framing 3-manifolds with bare hands

4. Radiologic image‐based statistical shape analysis of brain tumours

5. Gaussian Measures

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