On Hierarchical Models for Visual Recognition and Learning of Objects, Scenes, and Activities
Language: English
Published by Springer, 2014
Series: Book 12 of 378 - Studies in Systems, Decision and Control
- Hardcover
- Used
Seller: Book Dispensary, Concord, ON, CanadaBook Dispensary
AbeBooks seller since December 17, 2002
Condition: Used - As new
£ 46.26
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Add to basketItem description from seller
BRAND NEW hardcover with dust jacket, minor shelf/handling wear.
Seller Inventory # 154048
- Title
- On Hierarchical Models for Visual Recognition and Learning of Objects, Scenes, and Activities
- Author
- Spehr, Jens
- Publisher
- Springer
- Publication year
- 2014
- Condition
- As New
- Book Type
- Book
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3319113240
- ISBN 13
- 9783319113241
- Series
- Book 12 of 378: Studies in Systems, Decision and Control
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
Book Dispensary
Concord, ON, Canada
AbeBooks seller since December 17, 2002
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