Towards Fully Automatic Optimal Shape Modeling
(2008)- Abstract
- Shape models and the automatic building of such models have proven over
the last decades to be powerful tools in image segmentation and analysis.
This thesis makes contributions to this field.
The segmentation algorithm typically uses an objective function summing
up contributions from each sample point.
In this thesis this is replaced by the approximation of a surface
integral which improves the segmentation results.
Before building a model the shapes in the training set have to be aligned.
This is normally done using Procrustes analysis.
In the thesis an alignment method based on Minimum Decsription Length
(MDL) is examined and... (More) - Shape models and the automatic building of such models have proven over
the last decades to be powerful tools in image segmentation and analysis.
This thesis makes contributions to this field.
The segmentation algorithm typically uses an objective function summing
up contributions from each sample point.
In this thesis this is replaced by the approximation of a surface
integral which improves the segmentation results.
Before building a model the shapes in the training set have to be aligned.
This is normally done using Procrustes analysis.
In the thesis an alignment method based on Minimum Decsription Length
(MDL) is examined and the gradient of MDL is derived and used in the
optimization.
When trying to build optimal models by optimizing MDL there is a
tendency for the parameterizations to put most of their weight on small
parts of the shapes by doing a mutual reparameterization.
In this thesis this problem is solved by replacing the standard scalar
product with a formula that is invariant to mutual reparameterizations.
This is shown to result in better models.
To evaluate the quality of shape models, the standard measures have been
generality, specificity and compactness.
In this thesis, these measures are shown to have severe weaknesses.
An alternative measure called Ground Truth Correspondence Measure is
presented.
This measure is shown to perform better.
Typically, shape modeling assumes that the training set consists of
images where the shape has been segmented as a curve or a surface as a
preprocessing step.
In this thesis a method is introduced that does not need preprocessed
manually segmented data, automatically handles outliers/background and
missing data, and still produces strong models.
The algorithm makes all the decisions about what to include in the model
and what to consider as background and about what points in the
different images are to be considered to be corresponding.
This results in patch-based shape and appearance models generated fully
automatically. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1266177
- author
- Karlsson, Johan LU
- supervisor
-
- Karl Åström LU
- opponent
-
- Professor Larsen, Rasmus, Department of Mathematical Modelling, Technical University of Denmark
- organization
- publishing date
- 2008
- type
- Thesis
- publication status
- published
- subject
- keywords
- Parameterization Invariance, Benchmarking, Interpretation, Segmentation, Alignment, Shape Modeling, MDL
- pages
- 180 pages
- defense location
- Room MH:C, Centre for Mathematical Sciences, Sölvegatan 18, Lund University Faculty of Engineering
- defense date
- 2008-12-05 13:15:00
- ISBN
- 978-91-628-7226-7
- language
- English
- LU publication?
- yes
- id
- 8c859735-e58b-4c67-9029-9bf44f1a7ad1 (old id 1266177)
- date added to LUP
- 2016-04-04 13:52:04
- date last changed
- 2018-11-21 21:16:50
@phdthesis{8c859735-e58b-4c67-9029-9bf44f1a7ad1,
abstract = {{Shape models and the automatic building of such models have proven over<br/><br>
the last decades to be powerful tools in image segmentation and analysis.<br/><br>
This thesis makes contributions to this field.<br/><br>
<br/><br>
The segmentation algorithm typically uses an objective function summing<br/><br>
up contributions from each sample point.<br/><br>
In this thesis this is replaced by the approximation of a surface<br/><br>
integral which improves the segmentation results.<br/><br>
<br/><br>
Before building a model the shapes in the training set have to be aligned.<br/><br>
This is normally done using Procrustes analysis.<br/><br>
In the thesis an alignment method based on Minimum Decsription Length<br/><br>
(MDL) is examined and the gradient of MDL is derived and used in the<br/><br>
optimization.<br/><br>
<br/><br>
When trying to build optimal models by optimizing MDL there is a<br/><br>
tendency for the parameterizations to put most of their weight on small<br/><br>
parts of the shapes by doing a mutual reparameterization.<br/><br>
In this thesis this problem is solved by replacing the standard scalar<br/><br>
product with a formula that is invariant to mutual reparameterizations.<br/><br>
This is shown to result in better models.<br/><br>
<br/><br>
To evaluate the quality of shape models, the standard measures have been<br/><br>
generality, specificity and compactness.<br/><br>
In this thesis, these measures are shown to have severe weaknesses.<br/><br>
An alternative measure called Ground Truth Correspondence Measure is<br/><br>
presented.<br/><br>
This measure is shown to perform better.<br/><br>
<br/><br>
Typically, shape modeling assumes that the training set consists of<br/><br>
images where the shape has been segmented as a curve or a surface as a<br/><br>
preprocessing step.<br/><br>
In this thesis a method is introduced that does not need preprocessed<br/><br>
manually segmented data, automatically handles outliers/background and<br/><br>
missing data, and still produces strong models.<br/><br>
The algorithm makes all the decisions about what to include in the model<br/><br>
and what to consider as background and about what points in the<br/><br>
different images are to be considered to be corresponding.<br/><br>
This results in patch-based shape and appearance models generated fully<br/><br>
automatically.}},
author = {{Karlsson, Johan}},
isbn = {{978-91-628-7226-7}},
keywords = {{Parameterization Invariance; Benchmarking; Interpretation; Segmentation; Alignment; Shape Modeling; MDL}},
language = {{eng}},
school = {{Lund University}},
title = {{Towards Fully Automatic Optimal Shape Modeling}},
url = {{https://lup.lub.lu.se/search/files/6224205/1266182.pdf}},
year = {{2008}},
}