Optimizing Parametric Total Variation Models
(2009) IEEE International Conference on Computer Vision (ICCV), 2009 p.2240-2247- Abstract
- One of the key factors for the success of recent energy
minimization methods is that they seek to compute global
solutions. Even for non-convex energy functionals, optimization
methods such as graph cuts have proven to produce
high-quality solutions by iterative minimization based on
large neighborhoods, making them less vulnerable to local
minima. Our approach takes this a step further by enlarging
the search neighborhood with one dimension.
In this paper we consider binary total variation problems
that depend on an additional set of parameters. Examples
include:
(i) the Chan-Vese model that we solve globally
(ii) ratio and... (More) - One of the key factors for the success of recent energy
minimization methods is that they seek to compute global
solutions. Even for non-convex energy functionals, optimization
methods such as graph cuts have proven to produce
high-quality solutions by iterative minimization based on
large neighborhoods, making them less vulnerable to local
minima. Our approach takes this a step further by enlarging
the search neighborhood with one dimension.
In this paper we consider binary total variation problems
that depend on an additional set of parameters. Examples
include:
(i) the Chan-Vese model that we solve globally
(ii) ratio and constrained minimization which can be formulated
as parametric problems, and
(iii) variants of the Mumford-Shah functional.
Our approach is based on a recent theorem of Chambolle
which states that solving a one-parameter family of binary
problems amounts to solving a single convex variational
problem. We prove a generalization of this result and show
how it can be applied to parametric optimization. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1444558
- author
- Strandmark, Petter LU ; Kahl, Fredrik LU and Overgaard, Niels Christian LU
- organization
- publishing date
- 2009
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- segmentation, total variation, image analysis, optimization
- host publication
- [Host publication title missing]
- pages
- 2240 - 2247
- conference name
- IEEE International Conference on Computer Vision (ICCV), 2009
- conference location
- Kyoto, Japan
- conference dates
- 2009-09-27 - 2009-10-04
- external identifiers
-
- wos:000294955300289
- scopus:77953227527
- DOI
- 10.1109/ICCV.2009.5459464
- language
- English
- LU publication?
- yes
- id
- 559df996-3daa-4815-b12e-8d6c10cdb379 (old id 1444558)
- alternative location
- http://www.maths.lth.se/vision/publdb/reports/pdf/strandmark-kahl-etal-iccv-09.pdf
- date added to LUP
- 2016-04-04 13:23:17
- date last changed
- 2022-04-24 02:54:41
@inproceedings{559df996-3daa-4815-b12e-8d6c10cdb379,
abstract = {{One of the key factors for the success of recent energy<br/><br>
minimization methods is that they seek to compute global<br/><br>
solutions. Even for non-convex energy functionals, optimization<br/><br>
methods such as graph cuts have proven to produce<br/><br>
high-quality solutions by iterative minimization based on<br/><br>
large neighborhoods, making them less vulnerable to local<br/><br>
minima. Our approach takes this a step further by enlarging<br/><br>
the search neighborhood with one dimension.<br/><br>
In this paper we consider binary total variation problems<br/><br>
that depend on an additional set of parameters. Examples<br/><br>
include:<br/><br>
(i) the Chan-Vese model that we solve globally<br/><br>
(ii) ratio and constrained minimization which can be formulated<br/><br>
as parametric problems, and<br/><br>
(iii) variants of the Mumford-Shah functional.<br/><br>
Our approach is based on a recent theorem of Chambolle<br/><br>
which states that solving a one-parameter family of binary<br/><br>
problems amounts to solving a single convex variational<br/><br>
problem. We prove a generalization of this result and show<br/><br>
how it can be applied to parametric optimization.}},
author = {{Strandmark, Petter and Kahl, Fredrik and Overgaard, Niels Christian}},
booktitle = {{[Host publication title missing]}},
keywords = {{segmentation; total variation; image analysis; optimization}},
language = {{eng}},
pages = {{2240--2247}},
title = {{Optimizing Parametric Total Variation Models}},
url = {{https://lup.lub.lu.se/search/files/6107563/1444932.pdf}},
doi = {{10.1109/ICCV.2009.5459464}},
year = {{2009}},
}