ArticleProceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition2016
Shape Analysis with Hyperbolic Wasserstein Distance.
Article in Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 22 citations in OpenAlex.
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- Topological Embedding of Human Brain Networks with Applications to Dynamics of Temporal Lobe Epilepsy.ArXiv · 2024Article
- Topological state-space estimation of functional human brain networks.PLoS computational biology · 2024Article
- Unified topological inference for brain networks in temporal lobe epilepsy using the Wasserstein distance.NeuroImage · 2023Article
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- Predicting chemical shifts with graph neural networks.Chemical science · 2021Article
- Computing Univariate Neurodegenerative Biomarkers with Volumetric Optimal Transportation: A Pilot Study.Neuroinformatics · 2020Article
- Hyperbolic Wasserstein Distance for Shape Indexing.IEEE transactions on pattern analysis and machine intelligence · 2020Article
- Enhancing Diffusion MRI Measures By Integrating Grey and White Matter Morphometry With Hyperbolic Wasserstein Distance.Proceedings. IEEE International Symposium on Biomedical Imaging · 2017Article
- An Optimal Transportation based Univariate Neuroimaging Index.Proceedings. IEEE International Conference on Computer Vision · 2017Article
- Conformal invariants for multiply connected surfaces: Application to landmark curve-based brain morphometry analysis.Medical image analysis · 2017Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Shape space is an active research field in computer vision study. The shape distance defined in a shape space may provide a simple and refined index to represent a unique shape. Wasserstein distance defines a Riemannian metric for the Wasserstein space. It intrinsically measures the similarities between shapes and is robust to image noise. Thus it has the potential for the 3D shape indexing and classification research. While the algorithms for computing Wasserstein distance have been extensively studied, most of them only work for genus-0 surfaces. This paper proposes a novel framework to compute Wasserstein distance between general topological surfaces with hyperbolic metric. The computational algorithms are based on Ricci flow, hyperbolic harmonic map, and hyperbolic power Voronoi diagram and the method is general and robust. We apply our method to study human facial expression, longitudinal brain cortical morphometry with normal aging, and cortical shape classification in Alzheimer's disease (AD). Experimental results demonstrate that our method may be used as an effective shape index, which outperforms some other standard shape measures in our AD versus healthy control classification study.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.