東華大學圖書館 |

語系: 繁體中文

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

On hierarchical models for visual re...

Spehr, Jens.

FindBook

Google Book

Amazon

博客來

On hierarchical models for visual recognition and learning of objects, scenes, and activities

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	On hierarchical models for visual recognition and learning of objects, scenes, and activities/ by Jens Spehr.
作者:	Spehr, Jens.
出版者:	Cham :Springer International Publishing : : 2015.,
面頁冊數:	xvi, 199 p. :ill. (some col.), digital ;24 cm.
內容註:	Introduction -- Probabilistic Graphical Models -- Hierarchical Graphical Models -- Learning of Hierarchical Models.-Object Recognition -- Human Pose Estimation -- Scene Understanding for Intelligent Vehicles -- Conclusion.
Contained By:	Springer eBooks
標題:	Image processing - Digital techniques. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-11325-8
ISBN:	9783319113258 (electronic bk.)

On hierarchical models for visual recognition and learning of objects, scenes, and activities
Spehr, Jens.

On hierarchical models for visual recognition and learning of objects, scenes, and activities[electronic resource] /by Jens Spehr. - Cham :Springer International Publishing :2015. - xvi, 199 p. :ill. (some col.), digital ;24 cm. - Studies in systems, decision and control,v.112198-4182 ;. - Studies in systems, decision and control ;v.3..

Introduction -- Probabilistic Graphical Models -- Hierarchical Graphical Models -- Learning of Hierarchical Models.-Object Recognition -- Human Pose Estimation -- Scene Understanding for Intelligent Vehicles -- Conclusion.

In many computer vision applications, objects have to be learned and recognized in images or image sequences. This book presents new probabilistic hierarchical models that allow an efficient representation of multiple objects of different categories, scales, rotations, and views. The idea is to exploit similarities between objects and object parts in order to share calculations and avoid redundant information. Furthermore inference approaches for fast and robust detection are presented. These new approaches combine the idea of compositional and similarity hierarchies and overcome limitations of previous methods. Besides classical object recognition the book shows the use for detection of human poses in a project for gait analysis. The use of activity detection is presented for the design of environments for ageing, to identify activities and behavior patterns in smart homes. In a presented project for parking spot detection using an intelligent vehicle, the proposed approaches are used to hierarchically model the environment of the vehicle for an efficient and robust interpretation of the scene in real-time.

ISBN: 9783319113258 (electronic bk.)

Standard No.: 10.1007/978-3-319-11325-8doiSubjects--Topical Terms:

532550
Image processing
--Digital techniques.

LC Class. No.: TA1637

Dewey Class. No.: 006.6

On hierarchical models for visual recognition and learning of objects, scenes, and activities
LDR:02423nmm a2200337 a 4500 001 1993578
003 DE-He213
005 20150714140617.0
006 m d
007 cr nn 008maaau
008 151019s2015 gw s 0 eng d
020 $a 9783319113258 (electronic bk.)
020 $a 9783319113241 (paper)
024 7 $a 10.1007/978-3-319-11325-8 $2 doi
035 $a 978-3-319-11325-8
040 $a GP $c GP
041 0 $a eng
050 4 $a TA1637
072 7 $a TJFM1 $2 bicssc
072 7 $a TEC037000 $2 bisacsh
072 7 $a TEC004000 $2 bisacsh
082 0 4 $a 006.6 $2 23
090 $a TA1637 $b .S742 2015
100 1 $a Spehr, Jens. $3 2131889
245 1 0 $a On hierarchical models for visual recognition and learning of objects, scenes, and activities $h [electronic resource] / $c by Jens Spehr.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a xvi, 199 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in systems, decision and control, $x 2198-4182 ; $v v.11
505 0 $a Introduction -- Probabilistic Graphical Models -- Hierarchical Graphical Models -- Learning of Hierarchical Models.-Object Recognition -- Human Pose Estimation -- Scene Understanding for Intelligent Vehicles -- Conclusion.
520 $a In many computer vision applications, objects have to be learned and recognized in images or image sequences. This book presents new probabilistic hierarchical models that allow an efficient representation of multiple objects of different categories, scales, rotations, and views. The idea is to exploit similarities between objects and object parts in order to share calculations and avoid redundant information. Furthermore inference approaches for fast and robust detection are presented. These new approaches combine the idea of compositional and similarity hierarchies and overcome limitations of previous methods. Besides classical object recognition the book shows the use for detection of human poses in a project for gait analysis. The use of activity detection is presented for the design of environments for ageing, to identify activities and behavior patterns in smart homes. In a presented project for parking spot detection using an intelligent vehicle, the proposed approaches are used to hierarchically model the environment of the vehicle for an efficient and robust interpretation of the scene in real-time.
650 0 $a Image processing $x Digital techniques. $3 532550
650 0 $a Optical pattern recognition. $3 665526
650 1 4 $a Engineering. $3 586835
650 2 4 $a Robotics and Automation. $3 1066695
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Image Processing and Computer Vision. $3 891070
650 2 4 $a Pattern Recognition. $3 891045
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Studies in systems, decision and control ; $v v.3. $3 2058249
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-11325-8
950 $a Engineering (Springer-11647)