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UBC Theses and Dissertations
Probabilistic evidence combination for robust real time finger recognition and tracking Jennings, Cullen Frishman
Abstract
This thesis sets out a Bayesian approach to the robust combination of measurements from multiple sensors in different measurement spaces. Classical least squares optimization is used inside a sequential Monte Carlo approach to find the most likely local estimate. The local optimization speeds up the system, while the Monte Carlo approach improves robustness in finding the globally optimal solution. Models are simultaneously fit to all the sensor data. A statistical approach is taken to determine when inputs are failing and should be ignored. To demonstrate the overall approach described in this thesis, the 3D position and orientation of highly over-constrained models of deformable objects - fingers - are tracked. Accurate results are obtained by combining features of color and stereo range images. The multiple sources of information combined in this work include stereo range images, color segmentations, shape information and various constraints. The system is accurate and robust; it can continue to work even when one of the sources of information is completely failing. The system is practical in that it works in real time and can deal with complex moving backgrounds that have many edges, changing lighting, and other real world vision challenges.
Item Metadata
Title |
Probabilistic evidence combination for robust real time finger recognition and tracking
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
2002
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Description |
This thesis sets out a Bayesian approach to the robust combination of measurements
from multiple sensors in different measurement spaces. Classical least
squares optimization is used inside a sequential Monte Carlo approach to find the
most likely local estimate. The local optimization speeds up the system, while the
Monte Carlo approach improves robustness in finding the globally optimal solution.
Models are simultaneously fit to all the sensor data. A statistical approach is
taken to determine when inputs are failing and should be ignored.
To demonstrate the overall approach described in this thesis, the 3D position
and orientation of highly over-constrained models of deformable objects -
fingers - are tracked. Accurate results are obtained by combining features of color
and stereo range images. The multiple sources of information combined in this
work include stereo range images, color segmentations, shape information and
various constraints. The system is accurate and robust; it can continue to work
even when one of the sources of information is completely failing. The system is
practical in that it works in real time and can deal with complex moving backgrounds
that have many edges, changing lighting, and other real world vision
challenges.
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Extent |
17840877 bytes
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Genre | |
Type | |
File Format |
application/pdf
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Language |
eng
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Date Available |
2009-10-01
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Provider |
Vancouver : University of British Columbia Library
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Rights |
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.
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DOI |
10.14288/1.0051451
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
2002-05
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Campus | |
Scholarly Level |
Graduate
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Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.