語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Understanding Perceived Sense of Movement in Static Visuals Using Deep Learning.
紀錄類型:
書目-語言資料,手稿 : Monograph/item
正題名/作者:
Understanding Perceived Sense of Movement in Static Visuals Using Deep Learning./
作者:
Kale, Shravan.
面頁冊數:
1 online resource (97 pages)
附註:
Source: Masters Abstracts International, Volume: 80-05.
Contained By:
Masters Abstracts International80-05.
標題:
Computer science. -
電子資源:
click for full text (PQDT)
ISBN:
9780438693234
Understanding Perceived Sense of Movement in Static Visuals Using Deep Learning.
Kale, Shravan.
Understanding Perceived Sense of Movement in Static Visuals Using Deep Learning.
- 1 online resource (97 pages)
Source: Masters Abstracts International, Volume: 80-05.
Thesis (M.S.)--University of Oregon, 2018.
Includes bibliographical references
This thesis introduces the problem of learning the representation and the classification of the perceived sense of movement, defined as dynamism in static visuals. To solve the said problem, we study the definition, degree, and real-world implications of dynamism within the field of consumer psychology. We employ Deep Convolutional Neural Networks (DCNN) as a method to learn and predict dynamism in images. The novelty of the task, lead us to collect a dataset which we synthetically augmented for spatial invariance, using image processing techniques. We study the methods of transfer learning to transfer knowledge from another domain, as the size of our dataset was deemed to be inadequate. Our dataset is trained across different network architectures, and transfer learning techniques to find an optimal method for the task at hand. To show a real-world application of our work, we observe the correlation between the two visual stimuli, dynamism and emotions.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024
Mode of access: World Wide Web
ISBN: 9780438693234Subjects--Topical Terms:
573171
Computer science.
Subjects--Index Terms:
Computer visionIndex Terms--Genre/Form:
554714
Electronic books.
Understanding Perceived Sense of Movement in Static Visuals Using Deep Learning.
LDR
:02260ntm a22003617 4500
001
1143934
005
20240531084148.5
006
m o d
007
cr mn ---uuuuu
008
250605s2018 xx obm 000 0 eng d
020
$a
9780438693234
035
$a
(MiAaPQ)AAI10837419
035
$a
(MiAaPQ)oregon:12288
035
$a
AAI10837419
040
$a
MiAaPQ
$b
eng
$c
MiAaPQ
$d
NTU
100
1
$a
Kale, Shravan.
$3
1468748
245
1 0
$a
Understanding Perceived Sense of Movement in Static Visuals Using Deep Learning.
264
0
$c
2018
300
$a
1 online resource (97 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: 80-05.
500
$a
Publisher info.: Dissertation/Thesis.
500
$a
Advisor: Dou, Dejing.
502
$a
Thesis (M.S.)--University of Oregon, 2018.
504
$a
Includes bibliographical references
520
$a
This thesis introduces the problem of learning the representation and the classification of the perceived sense of movement, defined as dynamism in static visuals. To solve the said problem, we study the definition, degree, and real-world implications of dynamism within the field of consumer psychology. We employ Deep Convolutional Neural Networks (DCNN) as a method to learn and predict dynamism in images. The novelty of the task, lead us to collect a dataset which we synthetically augmented for spatial invariance, using image processing techniques. We study the methods of transfer learning to transfer knowledge from another domain, as the size of our dataset was deemed to be inadequate. Our dataset is trained across different network architectures, and transfer learning techniques to find an optimal method for the task at hand. To show a real-world application of our work, we observe the correlation between the two visual stimuli, dynamism and emotions.
533
$a
Electronic reproduction.
$b
Ann Arbor, Mich. :
$c
ProQuest,
$d
2024
538
$a
Mode of access: World Wide Web
650
4
$a
Computer science.
$3
573171
653
$a
Computer vision
653
$a
Deep learning
655
7
$a
Electronic books.
$2
local
$3
554714
690
$a
0984
710
2
$a
ProQuest Information and Learning Co.
$3
1178819
710
2
$a
University of Oregon.
$b
Department of Computer and Information Science.
$3
1186769
773
0
$t
Masters Abstracts International
$g
80-05.
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10837419
$z
click for full text (PQDT)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入