Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data

DOI10.1007/s11263-012-0592-xzbMath1270.68345OpenAlexW2065340506WikidataQ28681941 ScholiaQ28681941MaRDI QIDQ362068

Mohammad Hasan, M. Dambrine

Publication date: 20 August 2013

Published in: International Journal of Computer Vision (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3744347

zbMATH Keywords

growthlongitudinal datastatisticstime warpshape regressionspatiotemporal registration

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Applications of statistics (62P99) Machine vision and scene understanding (68T45)

Related Items (10)

Generation of tubular and membranous shape textures with curvature functionals ⋮ Nonparametric combination-based tests in dynamic shape analysis ⋮ Modeling Time-Varying Random Objects and Dynamic Networks ⋮ LRA: local rigid averaging of stretchable non-rigid shapes ⋮ Longitudinal shape analysis by using the spherical coordinates ⋮ Un-reduction in field theory ⋮ Estimation of a growth development with partial diffeomorphic mappings ⋮ A Coherent Framework for Learning Spatiotemporal Piecewise-Geodesic Trajectories from Longitudinal Manifold-Valued Data ⋮ Unnamed Item ⋮ Mechanistic Modeling of Longitudinal Shape Changes: Equations of Motion and Inverse Problems

Uses Software

• LDDMM

Cites Work

• Geodesic shooting for computational anatomy
• A Riemannian framework for tensor computing
• Landmark matching via large deformation diffeomorphisms
• Shape splines and stochastic shape evolutions: A second order point of view
• Stochastic algorithm for Bayesian mixture effect template estimation
• Variational problems on flows of diffeomorphisms for image matching

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