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Treffer: On Local Region Models and a Statistical Interpretation of the Piecewise Smooth Mumford-Shah Functional

Title:
On Local Region Models and a Statistical Interpretation of the Piecewise Smooth Mumford-Shah Functional
Authors:
BROX, Thomas, CREMERS, Daniel
Source:
Scale Space and Variational Methods in Computer VisionInternational journal of computer vision. 84(2):184-193
Publisher Information:
Heidelberg: Springer, 2009.
Publication Year:
2009
Physical Description:
print, 3/4 p
Original Material:
INIST-CNRS
Subject Terms:
Cognition, 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 statistique, Statistical analysis, Análisis estadístico, Approche probabiliste, Probabilistic approach, Enfoque probabilista, Calcul variationnel, Variational calculus, Cálculo de variaciones, Convolution, Convolución, Descente gradient, Gradient descent, Gradient bajada, Modélisation, Modeling, Modelización, Processus Gauss, Gaussian process, Proceso Gauss, Segmentation image, Image segmentation, Théorie locale, Local theory, Teoría local, Traitement image, Image processing, Procesamiento imagen, Regularization, Segmentation, Statistical methods, Variational methods
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Computer Vision Group, University of Bonn, Römerstr. 164, 53117 Bonn, Germany
ISSN:
0920-5691
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=21804592
Rights:
Copyright 2009 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.21804592
Database:
PASCAL Archive

Weitere Informationen

The Mumford-Shah functional is a general and quite popular variational model for image segmentation. In particular, it provides the possibility to represent regions by smooth approximations. In this paper, we derive a statistical interpretation of the full (piecewise smooth) Mumford-Shah functional by relating it to recent works on local region statistics. Moreover, we show that this statistical interpretation comes along with several implications. Firstly, one can derive extended versions of the Mumford-Shah functional including more general distribution models. Secondly, it leads to faster implementations. Finally, thanks to the analytical expression of the smooth approximation via Gaussian convolution, the coordinate descent can be replaced by a true gradient descent.