國立虎尾科技大學 |

Object Recognition in Videos Utilizing Hierarchical and Temporal Objectness with Deep Neural Networks.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Object Recognition in Videos Utilizing Hierarchical and Temporal Objectness with Deep Neural Networks./
作者:	Peng, Liang.
面頁冊數:	1 online resource (111 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
Contained By:	Dissertation Abstracts International79-01B(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355161427

Object Recognition in Videos Utilizing Hierarchical and Temporal Objectness with Deep Neural Networks.
Peng, Liang.

Object Recognition in Videos Utilizing Hierarchical and Temporal Objectness with Deep Neural Networks. - 1 online resource (111 pages)

Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

As the growth of mobile devices and social networks has been faster than ever, online image and video content have become truly ubiquitous today. Understanding of these images and videos, called vision, is one of the most primary ways for the human being to perceive the world. Computer vision, which refers to the study of enabling machines to see and understand the visual world, is fundamental in advancing Artificial Intelligence.

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

Mode of access: World Wide Web

ISBN: 9780355161427Subjects--Topical Terms:

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

554714
Electronic books.

Object Recognition in Videos Utilizing Hierarchical and Temporal Objectness with Deep Neural Networks.
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245 1 0 $a Object Recognition in Videos Utilizing Hierarchical and Temporal Objectness with Deep Neural Networks.
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300 $a 1 online resource (111 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
500 $a Adviser: Xiaojun Qi.
502 $a Thesis (Ph.D.) $c Utah State University $d 2017.
504 $a Includes bibliographical references
520 $a As the growth of mobile devices and social networks has been faster than ever, online image and video content have become truly ubiquitous today. Understanding of these images and videos, called vision, is one of the most primary ways for the human being to perceive the world. Computer vision, which refers to the study of enabling machines to see and understand the visual world, is fundamental in advancing Artificial Intelligence.
520 $a Object recognition, which is defined as the task of locating and recognizing object categories in images and videos, is a major research field in computer vision. Recent research in object recognition has achieved some significant improvement utilizing larger labeled data (e.g., ImageNet) and deep architecture of neural network algorithms (e.g.,Convolution Neutral Network, Restricted Boltzmann Machine, etc.). However, object recognition research using deep architectures has been mainly focused on images. Little has been done in videos, one of the fastest growing types of multimedia content. Video understanding, especially large-scale object detection in video, has applications in brand awareness, autonomous cars, augmented reality, etc.
520 $a The research presented in this dissertation proposes and demonstrates a novel system that automatically recognizes objects in videos by incorporating tracking, object detection and classification using deep neural networks. By utilizing temporal and spatial information, the proposed approach achieved the better object recognition performance than the prior state-of-the-art methods in terms of the average precision.
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
655 7 $a Electronic books. $2 local $3 554714
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690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Utah State University. $b Computer Science. $3 1179456
773 0 $t Dissertation Abstracts International $g 79-01B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10619161 $z click for full text (PQDT)