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Low-rank Based Algorithms for Rectif...
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City University of New York.
Low-rank Based Algorithms for Rectification, Repetition Detection and De-noising in Urban Images.
Record Type:
Language materials, manuscript : Monograph/item
Title/Author:
Low-rank Based Algorithms for Rectification, Repetition Detection and De-noising in Urban Images./
Author:
Liu, Juan.
Description:
1 online resource (104 pages)
Notes:
Source: Dissertation Abstracts International, Volume: 76-09(E), Section: B.
Contained By:
Dissertation Abstracts International76-09B(E).
Subject:
Computer science. -
Online resource:
click for full text (PQDT)
ISBN:
9781321752199
Low-rank Based Algorithms for Rectification, Repetition Detection and De-noising in Urban Images.
Liu, Juan.
Low-rank Based Algorithms for Rectification, Repetition Detection and De-noising in Urban Images.
- 1 online resource (104 pages)
Source: Dissertation Abstracts International, Volume: 76-09(E), Section: B.
Thesis (Ph.D.)--City University of New York, 2015.
Includes bibliographical references
In this thesis, we aim to solve the problem of automatic image rectification and repeated patterns detection on 2D urban images, using novel low-rank based techniques. Repeated patterns (such as windows, tiles, balconies and doors) are prominent and significant features in urban scenes.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018
Mode of access: World Wide Web
ISBN: 9781321752199Subjects--Topical Terms:
573171
Computer science.
Index Terms--Genre/Form:
554714
Electronic books.
Low-rank Based Algorithms for Rectification, Repetition Detection and De-noising in Urban Images.
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Low-rank Based Algorithms for Rectification, Repetition Detection and De-noising in Urban Images.
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Source: Dissertation Abstracts International, Volume: 76-09(E), Section: B.
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Adviser: Ioannis Stamos.
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Thesis (Ph.D.)--City University of New York, 2015.
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Includes bibliographical references
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In this thesis, we aim to solve the problem of automatic image rectification and repeated patterns detection on 2D urban images, using novel low-rank based techniques. Repeated patterns (such as windows, tiles, balconies and doors) are prominent and significant features in urban scenes.
520
$a
Detection of the periodic structures is useful in many applications such as photorealistic 3D reconstruction, 2D-to-3D alignment, facade parsing, city modeling, classification, navigation, visualization in 3D map environments, shape completion, cinematography and 3D games. However both of the image rectification and repeated patterns detection problems are challenging due to scene occlusions, varying illumination, pose variation and sensor noise. Therefore, detection of these repeated patterns becomes very important for city scene analysis.
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Given a 2D image of urban scene, we automatically rectify a facade image and extract facade textures first. Based on the rectified facade texture, we exploit novel algorithms that extract repeated patterns by using Kronecker product based modeling that is based on a solid theoretical foundation. We have tested our algorithms in a large set of images, which includes building facades from Paris, Hong Kong and New York.
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click for full text (PQDT)
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