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Graph based semi-supervised learning in computer vision

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Text
TitleInfo (ID = T-1)
Title
Graph based semi-supervised learning in computer vision
SubTitle
PartName
PartNumber
NonSort
Identifier
ETD_1357
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051017
Language (objectPart = )
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eng
Genre (authority = marcgt)
theses
Subject (ID = SBJ-1); (authority = RUETD)
Topic
Biomedical Engineering
Subject (ID = SBJ-2); (authority = ETD-LCSH)
Topic
Supervised learning (Machine learning)
Subject (ID = SBJ-3); (authority = ETD-LCSH)
Topic
Machine learning
Subject (ID = SBJ-4); (authority = ETD-LCSH)
Topic
Computer vision
Abstract
Machine learning from previous examples or knowledge is a key element in many image processing and pattern recognition tasks, e.g. clustering, segmentation, stereo matching, optical flow, tracking and object recognition. Acquiring that knowledge frequently requires human labeling of large data sets, which can be difficult and time-consuming to obtain. One way to ameliorate this task is to use Semi-supervised Learning (SSL), which combines both labeled and raw data and incorporates both global consistency (points in the same cluster are likely to have the same label) and local smoothness (nearby points are likely to have the same label). There are a number of vision tasks that can be solved efficiently and accurately using SSL. SSL has been applied extensively in clustering and image segmentation. In this dissertation, we will show that it is also suitable for stereo matching, optical flow and tracking problems.
Our novel algorithm has converted the stereo matching problem into a multi-label semi-supervised learning one. It is similar to a diffusion process, and we will show our approach has a closed-form solution for the multi-label problem. It sparks a new direction from the traditional energy minimization approach, such as Graph Cut or Belief Propagation. The occlusion area is detected using the matching confidence level, and solved with local fitting. Our results have been applied in the Middlebury Stereo database, and are within the top 20 best results in terms of accuracy and is considerably faster than the competing approaches.
We have also adapted our algorithm, and demonstrated its performance on optical flow problems. Again, our results are compared with the ground truth and state of the art on the Middlebury Flow database, and its advantages in accuracy as well as speed are demonstrated.
The above algorithm is also being used in our current NSF sponsored project, an Automated, Real-Time Identification and Monitoring Instrument for Reef Fish Communities, whose goal is to track and recognize tropical fish, initially in an aquarium and ultimately on a coral reef. Our approach, which combines background subtraction and optical flow, automatically finds the correct outline of multiple fish species in the field of view, and tracks the contour reliably over consecutive frames. Currently, near real-time results are being achieved, with a processing frame rate of 3-5 fps.
The recent progress in semi-supervised learning applied to image segmentation is also briefly reviewed.
PhysicalDescription
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electronic resource
Extent
x, 56 p. : ill.
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application/pdf
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text/xml
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references (p. 54-55)
Note (type = statement of responsibility)
by Ning Huang
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Huang
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Ning
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1976-
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author
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Ning Huang
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Wilder
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Joseph
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chair
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Advisory Committee
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Joseph Wilder
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Mammone
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Richard
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internal member
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Advisory Committee
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Richard Mammone
Name (ID = NAME-4); (type = personal)
NamePart (type = family)
Li
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John
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internal member
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Advisory Committee
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John K. Li
Name (ID = NAME-5); (type = personal)
NamePart (type = family)
Marsic
NamePart (type = given)
Ivan
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outside member
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Advisory Committee
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Ivan Marsic
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NamePart
Rutgers University
Role
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degree grantor
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Graduate School - New Brunswick
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school
OriginInfo
DateCreated (point = ); (qualifier = exact)
2009
DateOther (qualifier = exact); (type = degree)
2009-01
Location
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NjNbRU
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Identifier (type = doi)
doi:10.7282/T39G5N2C
Genre (authority = ExL-Esploro)
ETD doctoral
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The author owns the copyright to this work.
Copyright
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Copyright protected
Availability
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Open
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Detail
Non-exclusive ETD license
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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application/pdf
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application/x-tar
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1781760
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