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Multilinear algebra based techniques...

Rutgers The State University of New Jersey, School of Graduate Studies.

Multilinear algebra based techniques for foreground and background separation.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Multilinear algebra based techniques for foreground and background separation./
作者:	Tadimeti, Neha.
面頁冊數:	1 online resource (34 pages)
附註:	Source: Masters Abstracts International, Volume: 56-03.
Contained By:	Masters Abstracts International56-03(E).
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9781369635133

Multilinear algebra based techniques for foreground and background separation.
Tadimeti, Neha.

Multilinear algebra based techniques for foreground and background separation. - 1 online resource (34 pages)

Source: Masters Abstracts International, Volume: 56-03.

Thesis (M.S.)--Rutgers The State University of New Jersey, School of Graduate Studies, 2017.

Includes bibliographical references

The work presented in this thesis aims to understand the use of tensor algebra for background and foreground separation in videos. Specifically, it tries to explore the advantages of tensor-based approaches over the vector-based ones. In vector-based approaches, video frames are vectorized and concatenated into columns of a matrix for foreground and background separation. Through vectorization, one cannot explore the multi-dimensional aspect of video frames. Recent research has shown that tensor algebra can be helpful in extracting useful information from a multi-dimensional perspective. In this thesis, we propose two new algorithms which use tensor algebra to solve for background and foreground separation.

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

Mode of access: World Wide Web

ISBN: 9781369635133Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Multilinear algebra based techniques for foreground and background separation.
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100 1 $a Tadimeti, Neha. $3 1195499
245 1 0 $a Multilinear algebra based techniques for foreground and background separation.
264 0 $c 2017
300 $a 1 online resource (34 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: Masters Abstracts International, Volume: 56-03.
500 $a Adviser: Waheed U. Bajwa.
502 $a Thesis (M.S.)--Rutgers The State University of New Jersey, School of Graduate Studies, 2017.
504 $a Includes bibliographical references
520 $a The work presented in this thesis aims to understand the use of tensor algebra for background and foreground separation in videos. Specifically, it tries to explore the advantages of tensor-based approaches over the vector-based ones. In vector-based approaches, video frames are vectorized and concatenated into columns of a matrix for foreground and background separation. Through vectorization, one cannot explore the multi-dimensional aspect of video frames. Recent research has shown that tensor algebra can be helpful in extracting useful information from a multi-dimensional perspective. In this thesis, we propose two new algorithms which use tensor algebra to solve for background and foreground separation.
520 $a In the first part of the thesis, we develop a mini-batch extension to Online Tensor Robust Principal Component Analysis (OTRPCA). The proposed extension significantly reduces the computational time in comparison to OTRPCA. It is also shown that the accuracy levels of background separation are higher than OTRPCA for a decent mini-batch size. As the mini-batch size further increases, accuracy levels fall as the dictionary update is one-shot and non-iterative.
520 $a In the second part of the thesis, online vector-based Grassmanian Robust Adaptive Subspace Algorithm (GRASTA) is extended to tensor domain. The proposed Multi-Linear GRASTA (MLG) is also an online algorithm, thus suitable for real-time applications. Unlike the vector-based implementation, MLG explores the multi-dimensional nature of the video frames and solves for the separation problem across every dimension. MLG can process multiple frames at a time making it faster than other vector and tensor based separation algorithms. Detailed results are discussed which show that the accuracy of separation with MLG is competitive with the state-of-the-art.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
650 4 $a Computer science. $3 573171
650 4 $a Computer engineering. $3 569006
655 7 $a Electronic books. $2 local $3 554714
690 $a 0544
690 $a 0984
690 $a 0464
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Rutgers The State University of New Jersey, School of Graduate Studies. $b Graduate School - New Brunswick. $3 1193018
773 0 $t Masters Abstracts International $g 56-03(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10584765 $z click for full text (PQDT)

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