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Image Segmentation with Joint Regularization and Histogram Separation

Nilsson, David LU (2015) In Master’s Theses in Mathematical Sciences FMA820 20151
Mathematics (Faculty of Technology) and Numerical Analysis
Abstract
In this thesis optimization methods for image segmentation are studied. The common theme of all the methods is that we have a histogram model for appearance terms that we optimize jointly with smoothness. Recently it has been shown that if one assumes a histogram model for appearance, it is possible to optimize an approximation of the energy using only one graph cut, by ignoring the non-submodular volumetric penalty term. We show how to include the volumetric term using the Fast trust region framework. Fast trust region is a recently proposed method that is able to handle a large class of non-submodular energies by solving a sequence of graph cut problems. A comparison of these methods shows that Fast trust region typically obtains a lower... (More)
In this thesis optimization methods for image segmentation are studied. The common theme of all the methods is that we have a histogram model for appearance terms that we optimize jointly with smoothness. Recently it has been shown that if one assumes a histogram model for appearance, it is possible to optimize an approximation of the energy using only one graph cut, by ignoring the non-submodular volumetric penalty term. We show how to include the volumetric term using the Fast trust region framework. Fast trust region is a recently proposed method that is able to handle a large class of non-submodular energies by solving a sequence of graph cut problems. A comparison of these methods shows that Fast trust region typically obtains a lower energy value and higher segmentation quality, at the cost of requiring multiple graph cuts.

Furthermore, we extend the simple histogram term to the multi-class setting and show that it is possible to optimize it with alpha-expansions. This is applied to the problems of stereo depth estimation and geometric model fitting. (Less)
Please use this url to cite or link to this publication:
author
Nilsson, David LU
supervisor
organization
course
FMA820 20151
year
type
H2 - Master's Degree (Two Years)
subject
publication/series
Master’s Theses in Mathematical Sciences
report number
LUTFMA-3272-2015
ISSN
1404-6342
other publication id
2015:E12
language
English
id
5403412
date added to LUP
2015-06-18 12:11:26
date last changed
2015-06-18 12:11:26
@misc{5403412,
  abstract     = {In this thesis optimization methods for image segmentation are studied. The common theme of all the methods is that we have a histogram model for appearance terms that we optimize jointly with smoothness. Recently it has been shown that if one assumes a histogram model for appearance, it is possible to optimize an approximation of the energy using only one graph cut, by ignoring the non-submodular volumetric penalty term. We show how to include the volumetric term using the Fast trust region framework. Fast trust region is a recently proposed method that is able to handle a large class of non-submodular energies by solving a sequence of graph cut problems. A comparison of these methods shows that Fast trust region typically obtains a lower energy value and higher segmentation quality, at the cost of requiring multiple graph cuts.

Furthermore, we extend the simple histogram term to the multi-class setting and show that it is possible to optimize it with alpha-expansions. This is applied to the problems of stereo depth estimation and geometric model fitting.},
  author       = {Nilsson, David},
  issn         = {1404-6342},
  language     = {eng},
  note         = {Student Paper},
  series       = {Master’s Theses in Mathematical Sciences},
  title        = {Image Segmentation with Joint Regularization and Histogram Separation},
  year         = {2015},
}