• norsk
    • English
  • English 
    • norsk
    • English
  • Login
View Item 
  •   Home
  • Faculty of Mathematics and Natural Sciences
  • Department of Mathematics
  • Department of Mathematics
  • View Item
  •   Home
  • Faculty of Mathematics and Natural Sciences
  • Department of Mathematics
  • Department of Mathematics
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Efficient Global Minimization for the Multiphase Chan-Vese Model of Image Segmentation

Bae, Egil; Tai, Xue-Cheng
Peer reviewed, Journal article
Updated version
Thumbnail
View/Open
Efficient Global Minimization for the Multiphase.pdf (1006.Kb)
URI
https://hdl.handle.net/1956/5018
Date
2009
Metadata
Show full item record
Collections
  • Department of Mathematics [651]
Original version
https://doi.org/10.1007/978-3-642-03
Abstract
The Mumford-Shah model is an important variational image segmentation model. A popular multiphase level set approach, the Chan-Vese model, was developed as a numerical realization by representing the phases by several overlapping level set functions. Recently, a variant representation of the Chan-Vese model with binary level set functions was proposed. In both approaches, the gradient descent equations had to be solved numerically, a procedure which is slow and has the potential of getting stuck in a local minima. In this work, we develop an efficient and global minimization method for a discrete version of the level set representation of the Chan-Vese model with 4 regions (phases), based on graph cuts. If the average intensity values of the different regions are sufficiently evenly distributed, the energy function is submodular. It is shown theoretically and experimentally that the condition is expected to hold for the most commonly used data terms. We have also developed a method for minimizing nonsubmodular functions, that can produce global solutions in practice should the condition not be satisfied, which may happen for the L1 data term.
Description
In: Proc. Seventh International Conference on EnergyMinimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2009), pg.28-41, Lecture Notes in Computer Science, Springer, Berlin, 2009. (Extended journal version).
Publisher
Springer Verlag
Copyright
Copyright Springer-Verlag. All rights reserved.

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit
 

 

Browse

ArchiveCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsDocument TypesJournalsThis CollectionBy Issue DateAuthorsTitlesSubjectsDocument TypesJournals

My Account

Login

Statistics

View Usage Statistics

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit