Dictionary Learning for Two-Dimensional Kendall Shapes

DOI10.1137/19M126044XzbMath1455.94069arXiv1903.11356OpenAlexW3005113692MaRDI QIDQ5108467

Michael Unser, Julien Fageot, Virginie Uhlmann, Anna Song

Publication date: 4 May 2020

Published in: SIAM Journal on Imaging Sciences (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1903.11356

zbMATH Keywords

splinesinterpolating curvesKendall's shape space2D shape analysissparse dictionary learning

Mathematics Subject Classification ID

Numerical aspects of computer graphics, image analysis, and computational geometry (65D18) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Numerical interpolation (65D05) Real and complex geometry (51M99) Numerical solution to inverse problems in abstract spaces (65J22)

Cites Work

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• Functional and shape data analysis
• Extrinsic methods for coding and dictionary learning on Grassmann manifolds
• Cardinal interpolation and spline functions. V. The B-splines for cardinal Hermite interpolation
• B-spline snakes: a flexible tool for parametric contour detection
• Wavelet Statistics of Sparse and Self-Similar Images
• Sparse Modeling for Image and Vision Processing
• Statistical Shape Analysis, with Applications in R
• An Introduction to Sparse Stochastic Processes
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
• Sparse and Redundant Representations
• Shape Manifolds, Procrustean Metrics, and Complex Projective Spaces
• B-spline signal processing. I. Theory
• Dictionary Learning Algorithms for Sparse Representation
• Hermite Snakes With Control of Tangents
• Landmark-Based Shape Encoding and Sparse-Dictionary Learning in the Continuous Domain
• Linear Algebra and Optimization with Applications to Machine Learning
• Compressed sensing
• Riemannian geometry

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