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UBC Theses and Dissertations
Multi-image matching using invariant features Brown, Matthew Alun
Abstract
This thesis concerns the problems of automatic image stitching and 3D modelling from multiple views. These are basic problems of computer vision, with applications in robotics, architecture, industrial inspection, surveillance, computer graphics and film. Recent work has brought increasing automation to these tasks, but despite a large amount of progress, state-of-the-art algorithms still require some form of user input or assumptions about the image sequence. For example, the best image stitchers currently require an ordered set of input images, or user input to identify the matching images, before automatic registration can proceed. In this work we show how such tasks can be performed automatically and without any user input at all. We formulate the multi-image matching problem as one of finding all matching images, subject to the constraint that they are consistent views from a perspective camera. We use invariant features as a mechanism for finding correspondences, and indexing techniques to efficiently find matches between multiple views. We then find all sets of geometrically consistent feature matches, using a probabilistic model for verification. This allows us to identify each object or scene in the dataset using only the structure already present in the data. The major contributions of this thesis are the development of a system that can automatically recognise and stitch 2D panoramas in unordered image datasets, and a new class of invariant features for this purpose.
Item Metadata
Title |
Multi-image matching using invariant features
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
2005
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Description |
This thesis concerns the problems of automatic image stitching and 3D modelling from
multiple views. These are basic problems of computer vision, with applications in
robotics, architecture, industrial inspection, surveillance, computer graphics and film.
Recent work has brought increasing automation to these tasks, but despite a large
amount of progress, state-of-the-art algorithms still require some form of user input or
assumptions about the image sequence. For example, the best image stitchers currently
require an ordered set of input images, or user input to identify the matching images,
before automatic registration can proceed. In this work we show how such tasks can
be performed automatically and without any user input at all.
We formulate the multi-image matching problem as one of finding all matching
images, subject to the constraint that they are consistent views from a perspective
camera. We use invariant features as a mechanism for finding correspondences, and
indexing techniques to efficiently find matches between multiple views. We then find
all sets of geometrically consistent feature matches, using a probabilistic model for
verification. This allows us to identify each object or scene in the dataset using only
the structure already present in the data. The major contributions of this thesis are
the development of a system that can automatically recognise and stitch 2D panoramas
in unordered image datasets, and a new class of invariant features for this purpose.
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Genre | |
Type | |
Language |
eng
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Date Available |
2009-12-23
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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 |
10.14288/1.0051634
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
2005-05
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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.