Building blocks for computer vision with stochastic partial differential equations

DOI10.1007/s11263-008-0145-5zbMath1477.68410OpenAlexW1995135190MaRDI QIDQ847471

Tobias Preusser, Kai Krajsek, Hanno Scharr, Robert M. Kirby

Publication date: 16 February 2010

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

Full work available at URL: https://doi.org/10.1007/s11263-008-0145-5

zbMATH Keywords

image processingstochastic partial differential equationserror propagationrandom fieldspolynomial chaosstochastic Galerkin methodstochastic finite-element method

Mathematics Subject Classification ID

Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) PDEs with randomness, stochastic partial differential equations (35R60) Machine vision and scene understanding (68T45) PDEs in connection with computer science (35Q68)

Related Items (3)

Ambrosio-Tortorelli segmentation of stochastic images: model extensions, theoretical investigations and numerical methods ⋮ Segmentation of stochastic images using level set propagation with uncertain speed ⋮ On Backwardp(x)-Parabolic Equations for Image Enhancement

Uses Software

• VanHuffel

Cites Work

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