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Object Localization, Segmentation, a...

City University of New York.

Object Localization, Segmentation, and Classification in 3D Images.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Object Localization, Segmentation, and Classification in 3D Images./
作者:	Zelener, Allan.
面頁冊數:	1 online resource (93 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
標題:	Artificial intelligence. -
電子資源:	click for full text (PQDT)
ISBN:	9780355674828

Object Localization, Segmentation, and Classification in 3D Images.
Zelener, Allan.

Object Localization, Segmentation, and Classification in 3D Images. - 1 online resource (93 pages)

Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.

Thesis (Ph.D.)--City University of New York, 2018.

Includes bibliographical references

We address the problem of identifying objects of interest in 3D images as a set of related tasks involving localization of objects within a scene, segmentation of observed object instances from other scene elements, classifying detected objects into semantic categories, and estimating the 3D pose of detected objects within the scene. The increasing availability of 3D sensors motivates us to leverage large amounts of 3D data to train machine learning models to address these tasks in 3D images. Leveraging recent advances in deep learning has allowed us to develop models capable of addressing these tasks and optimizing these tasks jointly to reduce potential errors propagated when solving these tasks independently.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355674828Subjects--Topical Terms:

559380
Artificial intelligence.
Index Terms--Genre/Form:

554714
Electronic books.

Object Localization, Segmentation, and Classification in 3D Images.
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100 1 $a Zelener, Allan. $3 1184451
245 1 0 $a Object Localization, Segmentation, and Classification in 3D Images.
264 0 $c 2018
300 $a 1 online resource (93 pages)
336 $a text $b txt $2 rdacontent
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500 $a Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
500 $a Adviser: Ioannis Stamos.
502 $a Thesis (Ph.D.)--City University of New York, 2018.
504 $a Includes bibliographical references
520 $a We address the problem of identifying objects of interest in 3D images as a set of related tasks involving localization of objects within a scene, segmentation of observed object instances from other scene elements, classifying detected objects into semantic categories, and estimating the 3D pose of detected objects within the scene. The increasing availability of 3D sensors motivates us to leverage large amounts of 3D data to train machine learning models to address these tasks in 3D images. Leveraging recent advances in deep learning has allowed us to develop models capable of addressing these tasks and optimizing these tasks jointly to reduce potential errors propagated when solving these tasks independently.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Artificial intelligence. $3 559380
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0800
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a City University of New York. $b Computer Science. $3 1184450
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10745805 $z click for full text (PQDT)

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