Cargando…

On Hierarchical Models for Visual Recognition and Learning of Objects, Scenes, and Activities

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 expl...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Spehr, Jens (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2015.
Edición:1st ed. 2015.
Colección:Studies in Systems, Decision and Control, 11
Temas:
Acceso en línea:Texto Completo

MARC

LEADER 00000nam a22000005i 4500
001 978-3-319-11325-8
003 DE-He213
005 20220116021604.0
007 cr nn 008mamaa
008 141113s2015 sz | s |||| 0|eng d
020 |a 9783319113258  |9 978-3-319-11325-8 
024 7 |a 10.1007/978-3-319-11325-8  |2 doi 
050 4 |a TJ212-225 
050 4 |a TJ210.2-211.495 
072 7 |a TJFM  |2 bicssc 
072 7 |a TEC037000  |2 bisacsh 
072 7 |a TJFM  |2 thema 
082 0 4 |a 629.8  |2 23 
100 1 |a Spehr, Jens.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
245 1 0 |a On Hierarchical Models for Visual Recognition and Learning of Objects, Scenes, and Activities  |h [electronic resource] /  |c by Jens Spehr. 
250 |a 1st ed. 2015. 
264 1 |a Cham :  |b Springer International Publishing :  |b Imprint: Springer,  |c 2015. 
300 |a XV, 199 p. 107 illus., 92 illus. in color.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
490 1 |a Studies in Systems, Decision and Control,  |x 2198-4190 ;  |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 Control engineering. 
650 0 |a Robotics. 
650 0 |a Automation. 
650 0 |a Computational intelligence. 
650 0 |a Computer vision. 
650 0 |a Pattern recognition systems. 
650 1 4 |a Control, Robotics, Automation. 
650 2 4 |a Computational Intelligence. 
650 2 4 |a Computer Vision. 
650 2 4 |a Automated Pattern Recognition. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer Nature eBook 
776 0 8 |i Printed edition:  |z 9783319113265 
776 0 8 |i Printed edition:  |z 9783319113241 
776 0 8 |i Printed edition:  |z 9783319358628 
830 0 |a Studies in Systems, Decision and Control,  |x 2198-4190 ;  |v 11 
856 4 0 |u https://doi.uam.elogim.com/10.1007/978-3-319-11325-8  |z Texto Completo 
912 |a ZDB-2-ENG 
912 |a ZDB-2-SXE 
950 |a Engineering (SpringerNature-11647) 
950 |a Engineering (R0) (SpringerNature-43712)