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
Unsupervised computer vision for aer...
~
Zhang, Zhaoxiang.
FindBook
Google Book
Amazon
博客來
Unsupervised computer vision for aerospace systems = spacecraft pose estimation to infrastructure health monitoring /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Unsupervised computer vision for aerospace systems/ by Zhaoxiang Zhang.
其他題名:
spacecraft pose estimation to infrastructure health monitoring /
作者:
Zhang, Zhaoxiang.
出版者:
Singapore :Springer Nature Singapore : : 2025.,
面頁冊數:
vii, 200 p. :ill., digital ;24 cm.
內容註:
Introduction -- Jitter Estimation and Compensation in Spacecraft System -- Pose Estimation and Tracking for Space Objects -- Unsupervised Domain Adaptation for Autonomous Perception -- Safety Inspection of Aerospace Infrastructure -- Future Directions.
Contained By:
Springer Nature eBook
標題:
Computer vision. -
電子資源:
https://doi.org/10.1007/978-981-95-0023-9
ISBN:
9789819500239
Unsupervised computer vision for aerospace systems = spacecraft pose estimation to infrastructure health monitoring /
Zhang, Zhaoxiang.
Unsupervised computer vision for aerospace systems
spacecraft pose estimation to infrastructure health monitoring /[electronic resource] :by Zhaoxiang Zhang. - Singapore :Springer Nature Singapore :2025. - vii, 200 p. :ill., digital ;24 cm. - Scientific computation,2198-2589. - Scientific computation..
Introduction -- Jitter Estimation and Compensation in Spacecraft System -- Pose Estimation and Tracking for Space Objects -- Unsupervised Domain Adaptation for Autonomous Perception -- Safety Inspection of Aerospace Infrastructure -- Future Directions.
This book addresses perception and monitoring challenges in aerospace systems by employing innovative unsupervised learning techniques, thereby providing solutions for scenarios characterized by limited labelled data or dynamic environments. It explores practical methods such as domain adaptation for cross-modal pose estimation, causal inference for point cloud segmentation, and lightweight vision models optimized for edge computing. Key features include algorithm flowcharts, performance comparison tables, and real-world case studies covering planetary crater detection and spacecraft pose estimation. The integration of generative adversarial networks (GANs) for satellite jitter estimation and multistep adaptation strategies for defect detection offers actionable insights, supported by real industrial datasets, embedded hardware schematics, software code snippets, and optimization guidelines for real-time deployment. Engineers and researchers will obtain tools to enhance robustness across modalities and domains, ensuring generalizability in resource-constrained settings. This book serves as a valuable reference for aerospace engineers, computer vision specialists, and remote sensing practitioners and also empowers aerospace infrastructure inspectors adopting advanced vision technologies.
ISBN: 9789819500239
Standard No.: 10.1007/978-981-95-0023-9doiSubjects--Topical Terms:
540671
Computer vision.
LC Class. No.: TA1634
Dewey Class. No.: 006.37
Unsupervised computer vision for aerospace systems = spacecraft pose estimation to infrastructure health monitoring /
LDR
:02634nmm a2200337 a 4500
001
2414192
003
DE-He213
005
20250806173749.0
006
m d
007
cr nn 008maaau
008
260205s2025 si s 0 eng d
020
$a
9789819500239
$q
(electronic bk.)
020
$a
9789819500222
$q
(paper)
024
7
$a
10.1007/978-981-95-0023-9
$2
doi
035
$a
978-981-95-0023-9
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
TA1634
072
7
$a
UYQV
$2
bicssc
072
7
$a
COM016000
$2
bisacsh
072
7
$a
UYQV
$2
thema
082
0 4
$a
006.37
$2
23
090
$a
TA1634
$b
.Z63 2025
100
1
$a
Zhang, Zhaoxiang.
$3
3382507
245
1 0
$a
Unsupervised computer vision for aerospace systems
$h
[electronic resource] :
$b
spacecraft pose estimation to infrastructure health monitoring /
$c
by Zhaoxiang Zhang.
260
$a
Singapore :
$b
Springer Nature Singapore :
$b
Imprint: Springer,
$c
2025.
300
$a
vii, 200 p. :
$b
ill., digital ;
$c
24 cm.
490
1
$a
Scientific computation,
$x
2198-2589
505
0
$a
Introduction -- Jitter Estimation and Compensation in Spacecraft System -- Pose Estimation and Tracking for Space Objects -- Unsupervised Domain Adaptation for Autonomous Perception -- Safety Inspection of Aerospace Infrastructure -- Future Directions.
520
$a
This book addresses perception and monitoring challenges in aerospace systems by employing innovative unsupervised learning techniques, thereby providing solutions for scenarios characterized by limited labelled data or dynamic environments. It explores practical methods such as domain adaptation for cross-modal pose estimation, causal inference for point cloud segmentation, and lightweight vision models optimized for edge computing. Key features include algorithm flowcharts, performance comparison tables, and real-world case studies covering planetary crater detection and spacecraft pose estimation. The integration of generative adversarial networks (GANs) for satellite jitter estimation and multistep adaptation strategies for defect detection offers actionable insights, supported by real industrial datasets, embedded hardware schematics, software code snippets, and optimization guidelines for real-time deployment. Engineers and researchers will obtain tools to enhance robustness across modalities and domains, ensuring generalizability in resource-constrained settings. This book serves as a valuable reference for aerospace engineers, computer vision specialists, and remote sensing practitioners and also empowers aerospace infrastructure inspectors adopting advanced vision technologies.
650
0
$a
Computer vision.
$3
540671
650
0
$a
Aerospace engineering.
$3
1002622
650
0
$a
Space vehicles
$x
Electronic equipment.
$3
3384597
650
1 4
$a
Computer Vision.
$3
3538524
650
2 4
$a
Aerospace Technology and Astronautics.
$3
928116
650
2 4
$a
Machine Learning.
$3
3382522
650
2 4
$a
Artificial Intelligence.
$3
769149
710
2
$a
SpringerLink (Online service)
$3
836513
773
0
$t
Springer Nature eBook
830
0
$a
Scientific computation.
$3
1569496
856
4 0
$u
https://doi.org/10.1007/978-981-95-0023-9
950
$a
Engineering (SpringerNature-11647)
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
W9519647
電子資源
11.線上閱覽_V
電子書
EB TA1634
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