國立虎尾科技大學 |

Towards Temporal Semantic Scene Understanding.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Towards Temporal Semantic Scene Understanding./
Author:	Ghafarianzadeh, Mahsa.
Description:	1 online resource (152 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
Contained By:	Dissertation Abstracts International78-10B(E).
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9781369785784

Towards Temporal Semantic Scene Understanding.
Ghafarianzadeh, Mahsa.

Towards Temporal Semantic Scene Understanding. - 1 online resource (152 pages)

Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

This item is not available from ProQuest Dissertations & Theses.

The ability to quickly and accurately understand pixel-level scene semantics is a key capability required for various robotics applications such as autonomous driving. Until now, the temporal aspect of this problem has been largely overlooked. Therefore, the focus of research in this dissertation is to study the impact of temporal information in perception-related tasks and investigate whether it is useful to be included, more specifically for semantic scene understanding.

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

Mode of access: World Wide Web

ISBN: 9781369785784Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Towards Temporal Semantic Scene Understanding.
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100 1 $a Ghafarianzadeh, Mahsa. $3 1183649
245 1 0 $a Towards Temporal Semantic Scene Understanding.
264 0 $c 2017
300 $a 1 online resource (152 pages)
336 $a text $b txt $2 rdacontent
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500 $a Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
500 $a Adviser: Gabe Sibley.
502 $a Thesis (Ph.D.) $c University of Colorado at Boulder $d 2017.
504 $a Includes bibliographical references
506 $a This item is not available from ProQuest Dissertations & Theses.
520 $a The ability to quickly and accurately understand pixel-level scene semantics is a key capability required for various robotics applications such as autonomous driving. Until now, the temporal aspect of this problem has been largely overlooked. Therefore, the focus of research in this dissertation is to study the impact of temporal information in perception-related tasks and investigate whether it is useful to be included, more specifically for semantic scene understanding.
520 $a In this thesis, we first propose a set of novel techniques for unsupervised spatio-temporal segmentation in video sequences to obtain regions that are coherent in space and time. We then extend our method to exploit other strong cues present in the scene such as the depth signal or object parts to further improve the accuracy. The bottleneck in studying the temporal data is caused by both the limitations in computing resources and/or the lack of existing comprehensive labeled data. We tackle these issues by introducing a simple and efficient unsupervised label propagation algorithm that transfers the pixel-wise semantic labels from a groundtruth frame to its adjacent neighbor frames and produces auxiliary temporal groundtruth. Finally, we take a further step towards the ultimate goal of holistic scene understanding and present a deep, recurrent multi-scale network that is capable of leveraging the temporal information present in the video data. We show that our model can be easily extended to the related problem of prediction to estimate the expected semantics of the scene a small number of frames into the future. We achieve promising state-of-the-art results on various datasets and prove that our temporal approach is superior to the non-temporal baseline.
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 Artificial intelligence. $3 559380
650 4 $a Robotics. $3 561941
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0800
690 $a 0771
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Colorado at Boulder. $b Computer Science. $3 1180184
773 0 $t Dissertation Abstracts International $g 78-10B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10271366 $z click for full text (PQDT)