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Information selection and fusion in vision systems

Linda Tessens UGent (2010)
abstract
Handling the enormous amounts of data produced by data-intensive imaging systems, such as multi-camera surveillance systems and microscopes, is technically challenging. While image and video compression help to manage the data volumes, they do not address the basic problem of information overflow. In this PhD we tackle the problem in a more drastic way. We select information of interest to a specific vision task, and discard the rest. We also combine data from different sources into a single output product, which presents the information of interest to end users in a suitable, summarized format. We treat two types of vision systems. The first type is conventional light microscopes. During this PhD, we have exploited for the first time the potential of the curvelet transform for image fusion for depth-of-field extension, allowing us to combine the advantages of multi-resolution image analysis for image fusion with increased directional sensitivity. As a result, the proposed technique clearly outperforms state-of-the-art methods, both on real microscopy data and on artificially generated images. The second type is camera networks with overlapping fields of view. To enable joint processing in such networks, inter-camera communication is essential. Because of infrastructure costs, power consumption for wireless transmission, etc., transmitting high-bandwidth video streams between cameras should be avoided. Fortunately, recently designed 'smart cameras', which have on-board processing and communication hardware, allow distributing the required image processing over the cameras. This permits compactly representing useful information from each camera. We focus on representing information for people localization and observation, which are important tools for statistical analysis of room usage, quick localization of people in case of building fires, etc. To further save bandwidth, we select which cameras should be involved in a vision task and transmit observations only from the selected cameras. We provide an information-theoretically founded framework for general purpose camera selection based on the Dempster-Shafer theory of evidence. Applied to tracking, it allows tracking people using a dynamic selection of as little as three cameras with the same accuracy as when using up to ten cameras.
Please use this url to cite or link to this publication:
author
promoter
UGent and UGent
organization
alternative title
Informatieselectie en -fusie in visuele systemen
year
type
dissertation (monograph)
subject
keyword
tracking, camera networks, image denoising, smart cameras
pages
XVI, 168 pages
publisher
Ghent University. Faculty of Engineering
place of publication
Ghent, Belgium
defense location
Gent : Faculteit Ingenieurswetenschappen (Jozef Plateauzaal)
defense date
2010-10-21 17:00
ISBN
9789085783855
language
English
UGent publication?
yes
classification
D1
copyright statement
I have retained and own the full copyright for this publication
id
1860591
handle
http://hdl.handle.net/1854/LU-1860591
date created
2011-07-24 18:10:40
date last changed
2011-07-26 11:22:14
@phdthesis{1860591,
  abstract     = {Handling the enormous amounts of data produced by data-intensive imaging systems, such as multi-camera surveillance systems and microscopes, is technically challenging.
While image and video compression help to manage the data volumes, they do not address the basic problem of information overflow. In this PhD we tackle the problem in a more drastic way. We select information of interest to a specific vision task, and discard the rest. We also combine data from different sources into a single output product, which presents the information of interest to end users in a suitable, summarized format. 
We treat two types of vision systems. 
The first type is conventional light microscopes. During this PhD, we have exploited for the first time the potential of the curvelet transform for image fusion for depth-of-field extension, allowing us to combine the advantages of multi-resolution image analysis for image fusion with increased directional sensitivity. As a result, the proposed technique clearly outperforms state-of-the-art methods, both on real microscopy data and on artificially generated images.
The second type is camera networks with overlapping fields of view. To enable joint processing in such networks, inter-camera communication is essential. Because of infrastructure costs, power consumption for wireless transmission, etc., transmitting high-bandwidth video streams between cameras should be avoided. Fortunately, recently designed 'smart cameras', which have on-board processing and communication hardware, allow distributing the required image processing over the cameras. This permits compactly representing useful information from each camera. We focus on representing information for people localization and observation, which are important tools for statistical analysis of room usage, quick localization of people in case of building fires, etc.
To further save bandwidth, we select which cameras should be involved in a vision task and transmit observations only from the selected cameras. We provide an information-theoretically founded framework for general purpose camera selection based on the Dempster-Shafer theory of evidence. Applied to tracking, it allows tracking people using a dynamic selection of as little as three cameras with the same accuracy as when using up to ten cameras.},
  author       = {Tessens, Linda},
  isbn         = {9789085783855},
  keyword      = {tracking,camera networks,image denoising,smart cameras},
  language     = {eng},
  pages        = {XVI, 168},
  publisher    = {Ghent University. Faculty of Engineering},
  school       = {Ghent University},
  title        = {Information selection and fusion in vision systems},
  year         = {2010},
}

Chicago
Tessens, Linda. 2010. “Information Selection and Fusion in Vision Systems”. Ghent, Belgium: Ghent University. Faculty of Engineering.
APA
Tessens, L. (2010). Information selection and fusion in vision systems. Ghent University. Faculty of Engineering, Ghent, Belgium.
Vancouver
1.
Tessens L. Information selection and fusion in vision systems. [Ghent, Belgium]: Ghent University. Faculty of Engineering; 2010.
MLA
Tessens, Linda. “Information Selection and Fusion in Vision Systems.” 2010 : n. pag. Print.