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Low-rank Based Algorithms for Rectif...

City University of New York.

Low-rank Based Algorithms for Rectification, Repetition Detection and De-noising in Urban Images.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Low-rank Based Algorithms for Rectification, Repetition Detection and De-noising in Urban Images./
作者:	Liu, Juan.
面頁冊數:	1 online resource (104 pages)
附註:	Source: Dissertation Abstracts International, Volume: 76-09(E), Section: B.
Contained By:	Dissertation Abstracts International76-09B(E).
標題:	Computer science. -
電子資源:	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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040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Liu, Juan. $3 1189635
245 1 0 $a Low-rank Based Algorithms for Rectification, Repetition Detection and De-noising in Urban Images.
264 0 $c 2015
300 $a 1 online resource (104 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 76-09(E), Section: B.
500 $a Adviser: Ioannis Stamos.
502 $a Thesis (Ph.D.)--City University of New York, 2015.
504 $a Includes bibliographical references
520 $a 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.
520 $a 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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Architecture. $3 555123
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0729
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a City University of New York. $b Computer Science. $3 1184450
773 0 $t Dissertation Abstracts International $g 76-09B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3703410 $z click for full text (PQDT)

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