A Gauss-Newton method for the integration of spatial normal fields in shape space

DOI10.1007/s10851-011-0311-1zbMath1255.68204OpenAlexW1999298886MaRDI QIDQ1932964

Jonathan Balzer

Publication date: 22 January 2013

Published in: Journal of Mathematical Imaging and Vision (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10851-011-0311-1

zbMATH Keywords

minimal surfaceintegrationshape spaceGauss-Newton methodnormal mapshape Hessiannormal fieldSobolev flow

Mathematics Subject Classification ID

Computing methodologies for image processing (68U10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Machine vision and scene understanding (68T45)

Related Items (3)

Normal integration: a survey ⋮ Attracting and repelling Lagrangian coherent structures from a single computation ⋮ Regularized reconstruction of a surface from its measured gradient field

Uses Software

• Gmsh
• CHOLMOD
• OpenMesh

Cites Work

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• Image Segmentation Using Active Contours: Calculus of Variations or Shape Gradients?
• A Second Order Shape Optimization Approach for Image Segmentation
• Numerical Solution of Partial Differential Equations
• Variational, Geometric, and Level Set Methods in Computer Vision
• Shapes and diffeomorphisms

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