語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Deep Person Re-identification Using ...
~
Moosavi, Shahla.
FindBook
Google Book
Amazon
博客來
Deep Person Re-identification Using Supervised Learning with Ranking Method.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Deep Person Re-identification Using Supervised Learning with Ranking Method./
作者:
Moosavi, Shahla.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:
66 p.
附註:
Source: Masters Abstracts International, Volume: 80-12.
Contained By:
Masters Abstracts International80-12.
標題:
Computer Engineering. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13879924
ISBN:
9781392181225
Deep Person Re-identification Using Supervised Learning with Ranking Method.
Moosavi, Shahla.
Deep Person Re-identification Using Supervised Learning with Ranking Method.
- Ann Arbor : ProQuest Dissertations & Theses, 2019 - 66 p.
Source: Masters Abstracts International, Volume: 80-12.
Thesis (M.S.)--The University of Texas at San Antonio, 2019.
This item must not be sold to any third party vendors.
In the present world packed with cameras at every corner the data generated from digital surveillance has become so substantial that it is impossible for human operators to make sense out of. Correspondingly, the intensification of machine vision algorithms that can invest through such data and return consequential perceptions has offered some solutions. Computer Vision techniques such as face detection/recognition and person re-identification has proven their worth into cameras and social medias. Person re-identification is correlating with images of the same person yet taken from different cameras or from the same camera in different incidents. Simply put, allocating a person in multi-camera setting. Us humans, we are easily able to re-identify others by easily descriptors based on the person's appearance (face, height, and build, clothing, hair style, walkingpattern, etc.) but this easy task, is more difficult for a machine to unscramble.
ISBN: 9781392181225Subjects--Topical Terms:
1567821
Computer Engineering.
Deep Person Re-identification Using Supervised Learning with Ranking Method.
LDR
:02016nmm a2200325 4500
001
2207152
005
20190913102457.5
008
201008s2019 ||||||||||||||||| ||eng d
020
$a
9781392181225
035
$a
(MiAaPQ)AAI13879924
035
$a
(MiAaPQ)utsa:12802
035
$a
AAI13879924
040
$a
MiAaPQ
$c
MiAaPQ
100
1
$a
Moosavi, Shahla.
$3
3434092
245
1 0
$a
Deep Person Re-identification Using Supervised Learning with Ranking Method.
260
1
$a
Ann Arbor :
$b
ProQuest Dissertations & Theses,
$c
2019
300
$a
66 p.
500
$a
Source: Masters Abstracts International, Volume: 80-12.
500
$a
Publisher info.: Dissertation/Thesis.
500
$a
Advisor: Rad, Paul;Huang, Yufei.
502
$a
Thesis (M.S.)--The University of Texas at San Antonio, 2019.
506
$a
This item must not be sold to any third party vendors.
520
$a
In the present world packed with cameras at every corner the data generated from digital surveillance has become so substantial that it is impossible for human operators to make sense out of. Correspondingly, the intensification of machine vision algorithms that can invest through such data and return consequential perceptions has offered some solutions. Computer Vision techniques such as face detection/recognition and person re-identification has proven their worth into cameras and social medias. Person re-identification is correlating with images of the same person yet taken from different cameras or from the same camera in different incidents. Simply put, allocating a person in multi-camera setting. Us humans, we are easily able to re-identify others by easily descriptors based on the person's appearance (face, height, and build, clothing, hair style, walkingpattern, etc.) but this easy task, is more difficult for a machine to unscramble.
590
$a
School code: 1283.
650
4
$a
Computer Engineering.
$3
1567821
650
4
$a
Artificial intelligence.
$3
516317
690
$a
0464
690
$a
0800
710
2
$a
The University of Texas at San Antonio.
$b
Electrical & Computer Engineering.
$3
1018585
773
0
$t
Masters Abstracts International
$g
80-12.
790
$a
1283
791
$a
M.S.
792
$a
2019
793
$a
English
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13879924
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9383701
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入