Treffer: Discrete orthogonal decomposition and variational fluid flow estimation

Title:
Discrete orthogonal decomposition and variational fluid flow estimation
Authors:
JING YUAN, RUHNAU, Paul, MEMIN, Etinne, SCHNÖRR, Christoph
Source:
Scale space and PDE methods in computer vision (Hofgeismar, 7-9 April 2005)Lecture notes in computer science. :267-278
Publisher Information:
Berlin: Springer, 2005.
Publication Year:
2005
Physical Description:
print, 17 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Reconnaissance des formes. Traitement numérique des images. Géométrie algorithmique, Pattern recognition. Digital image processing. Computational geometry, Analyse forme, Pattern analysis, Análisis forma, Analyse mouvement, Motion analysis, Análisis movimiento, Calcul variationnel, Variational calculus, Cálculo de variaciones, Ecoulement, Flow(fluid), Flujo, Espace vectoriel, Vector space, Espacio vectorial, Estimation mouvement, Motion estimation, Estimación movimiento, Fonction potentiel, Potential function, Función potencial, Mesure déplacement, Displacement measurement, Medición desplazamiento, Modélisation, Modeling, Modelización, Méthode différence finie, Finite difference method, Método diferencia finita, Méthode élément fini, Finite element method, Método elemento finito, Polynôme orthogonal, Orthogonal polynomial, Polinomio ortogonal, Séquence image, Image sequence, Secuencia imagen, Traitement image, Image processing, Procesamiento imagen, Vision ordinateur, Computer vision, Visión ordenador
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Mathematics and Computer Science, Computer Vision, Graphics, and Pattern Recognition Group, University of Mannheim, 68131 Mannheim, Germany
IRISA Rennes, Campus Universitaire de Beaulieu, 35042 Rennes, France
ISSN:
0302-9743
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=16894628
Rights:
Copyright 2005 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.16894628
Database:
PASCAL Archive

Weitere Informationen

The decomposition of motion vector fields into components of orthogonal subspaces is an important representation for both the analysis and the variational estimation of complex motions. Common finite differencing or finite element methods, however, do not preserve the basic identities of vector analysis. Therefore, we introduce in this paper the mimetic finite difference method for the estimation of fluid flows from image sequences. Using this discrete setting, we represent the motion components directly in terms of potential functions which are useful for motion pattern analysis. Additionally, we analyze well-posedness which has been lacking in previous work. Experimental results, including hard physical constraints like vanishing divergence of the flow, validate the theory.