Shape Modeling by Optimising Description Length Using Gradients and Parameterisation Invariance

DOI10.1007/978-3-642-20236-0_4zbMath1420.62237OpenAlexW1746317705MaRDI QIDQ5195003

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Publication date: 17 September 2019

Published in: Analysis for Science, Engineering and Beyond (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-642-20236-0_4

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (2)

Shape Modeling by Optimising Description Length Using Gradients and Parameterisation Invariance ⋮ On the Bijectivity of Thin-Plate Splines

Cites Work

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• A Mathematical Theory of Communication
• Modeling by shortest data description
• Generalized Procrustes analysis
• Size and shape spaces for landmark data in two dimensions (with discussion)
• The statistical theory of shape
• A Minimum Description Length Approach to Statistical Shape Modelling
• Derivatives of Eigenvalues and Eigenvectors of Matrix Functions
• The Complex Watson Distribution and Shape Analysis
• Shape Modeling by Optimising Description Length Using Gradients and Parameterisation Invariance

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