ArticleIEEE transactions on pattern analysis and machine intelligence2017
Hyperbolic Harmonic Mapping for Surface Registration.
Article in IEEE transactions on pattern analysis and machine intelligence, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- High genus surface parameterization using the Euclidean Ricci flow method.Scientific reports · 2025Article
- Hyperbolic Wasserstein Distance for Shape Indexing.IEEE transactions on pattern analysis and machine intelligence · 2020Article
- Optimizing Visual Cortex Parameterization with Error-Tolerant Teichmüller Map in Retinotopic Mapping.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2020Article
- Hierarchical spherical deformation for cortical surface registration.Medical image analysis · 2019Article
- Predicting Imminent Progression to Clinically Significant Memory Decline Using Volumetric MRI and FDG PET.Journal of Alzheimer's disease : JAD · 2018Article
- Intrinsic 3D Dynamic Surface Tracking based on Dynamic Ricci Flow and Teichmüller Map.Proceedings. IEEE International Conference on Computer Vision · 2017Article
- Towards a Holistic Cortical Thickness Descriptor: Heat Kernel-Based Grey Matter Morphology Signatures.NeuroImage · 2017Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Automatic computation of surface correspondence via harmonic map is an active research field in computer vision, computer graphics and computational geometry. It may help document and understand physical and biological phenomena and also has broad applications in biometrics, medical imaging and motion capture industries. Although numerous studies have been devoted to harmonic map research, limited progress has been made to compute a diffeomorphic harmonic map on general topology surfaces with landmark constraints. This work conquers this problem by changing the Riemannian metric on the target surface to a hyperbolic metric so that the harmonic mapping is guaranteed to be a diffeomorphism under landmark constraints. The computational algorithms are based on Ricci flow and nonlinear heat diffusion methods. The approach is general and robust. We employ our algorithm to study the constrained surface registration problem which applies to both computer vision and medical imaging applications. Experimental results demonstrate that, by changing the Riemannian metric, the registrations are always diffeomorphic and achieve relatively high performance when evaluated with some popular surface registration evaluation standards.
Identifiers
What OpenQuestion holds
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.