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Geometric uncertainty models for correspondence problems in digital image processing

Kristof Teelen UGent (2010)
abstract
Many recent advances in technology rely heavily on the correct interpretation of an enormous amount of visual information. All available sources of visual data (e.g. cameras in surveillance networks, smartphones, game consoles) must be adequately processed to retrieve the most interesting user information. Therefore, computer vision and image processing techniques gain significant interest at the moment, and will do so in the near future. Most commonly applied image processing algorithms require a reliable solution for correspondence problems. The solution involves, first, the localization of corresponding points -visualizing the same 3D point in the observed scene- in the different images of distinct sources, and second, the computation of consistent geometric transformations relating correspondences on scene objects. This PhD presents a theoretical framework for solving correspondence problems with geometric features (such as points and straight lines) representing rigid objects in image sequences of complex scenes with static and dynamic cameras. The research focuses on localization uncertainty due to errors in feature detection and measurement, and its effect on each step in the solution of a correspondence problem. Whereas most other recent methods apply statistical-based models for spatial localization uncertainty, this work considers a novel geometric approach. Localization uncertainty is modeled as a convex polygonal region in the image space. This model can be efficiently propagated throughout the correspondence finding procedure. It allows for an easy extension toward transformation uncertainty models, and to infer confidence measures to verify the reliability of the outcome in the correspondence framework. Our procedure aims at finding reliable consistent transformations in sets of few and ill-localized features, possibly containing a large fraction of false candidate correspondences. The evaluation of the proposed procedure in practical correspondence problems shows that correct consistent correspondence sets are returned in over 95% of the experiments for small sets of 10-40 features contaminated with up to 400% of false positives and 40% of false negatives. The presented techniques prove to be beneficial in typical image processing applications, such as image registration and rigid object tracking.
Please use this url to cite or link to this publication:
author
promoter
UGent and UGent
organization
alternative title
Geometrische onzekerheidsmodellen voor correspondentieproblemen in digitale beeldverwerking
year
type
dissertation (monograph)
subject
keyword
correspondence problems, computer vision, image processing, uncertainty
pages
XXVIII, 276 pages
publisher
Ghent University. Faculty of Engineering
place of publication
Ghent, Belgium
defense location
Gent : Faculteit Ingenieurswetenschappen (Jozef Plateauzaal)
defense date
2010-11-09 15:30
ISBN
9789085783886
language
English
UGent publication?
yes
classification
D1
copyright statement
I have retained and own the full copyright for this publication
id
1850743
handle
http://hdl.handle.net/1854/LU-1850743
date created
2011-07-01 10:47:23
date last changed
2011-07-04 11:41:26
@phdthesis{1850743,
  abstract     = {Many recent advances in technology rely heavily on the correct interpretation of an enormous amount of visual information. All available sources of visual data (e.g. cameras in surveillance networks, smartphones, game consoles) must be adequately processed to retrieve the most interesting user information. Therefore, computer vision and image processing techniques gain significant interest at the moment, and will do so in the near future.
Most commonly applied image processing algorithms require a reliable solution for correspondence problems. The solution involves, first, the localization of corresponding points -visualizing the same 3D point in the observed scene- in the different images of distinct sources, and second, the computation of consistent geometric transformations relating correspondences on scene objects.
This PhD presents a theoretical framework for solving correspondence problems with geometric features (such as points and straight lines) representing rigid objects in image sequences of complex scenes with static and dynamic cameras. The research focuses on localization uncertainty due to errors in feature detection and measurement, and its effect on each step in the solution of a correspondence problem. 
Whereas most other recent methods apply statistical-based models for spatial localization uncertainty, this work considers a novel geometric approach. Localization uncertainty is modeled as a convex polygonal region in the image space. This model can be efficiently propagated throughout the correspondence finding procedure. It allows for an easy extension toward transformation uncertainty models, and to infer confidence measures to verify the reliability of the outcome in the correspondence framework. Our procedure aims at finding reliable consistent transformations in sets of few and ill-localized features, possibly containing a large fraction of false candidate correspondences.
The evaluation of the proposed procedure in practical correspondence problems shows that correct consistent correspondence sets are returned in over 95\% of the experiments for small sets of 10-40 features contaminated with up to 400\% of false positives and 40\% of false negatives. The presented techniques prove to be beneficial in typical image processing applications, such as image registration and rigid object tracking.},
  author       = {Teelen, Kristof},
  isbn         = {9789085783886},
  keyword      = {correspondence problems,computer vision,image processing,uncertainty},
  language     = {eng},
  pages        = {XXVIII, 276},
  publisher    = {Ghent University. Faculty of Engineering},
  school       = {Ghent University},
  title        = {Geometric uncertainty models for correspondence problems in digital image processing},
  year         = {2010},
}

Chicago
Teelen, Kristof. 2010. “Geometric Uncertainty Models for Correspondence Problems in Digital Image Processing”. Ghent, Belgium: Ghent University. Faculty of Engineering.
APA
Teelen, K. (2010). Geometric uncertainty models for correspondence problems in digital image processing. Ghent University. Faculty of Engineering, Ghent, Belgium.
Vancouver
1.
Teelen K. Geometric uncertainty models for correspondence problems in digital image processing. [Ghent, Belgium]: Ghent University. Faculty of Engineering; 2010.
MLA
Teelen, Kristof. “Geometric Uncertainty Models for Correspondence Problems in Digital Image Processing.” 2010 : n. pag. Print.