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...
Saved in:
| Main Author: | |
|---|---|
| Corporate Author: | |
| Format: | eBook |
| Language: | English |
| Published: |
Cham :
Springer International Publishing : Imprint: Springer,
2015.
|
| Series: | Studies in Systems, Decision and Control,
11 |
| Subjects: | |
| Online Access: | Click here to view the full text content |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| Physical Description: | 1 online resource (xv, 199 pages) 107 illustrations, 92 illustrations in color |
| ISBN: | 9783319113258 |
| ISSN: | 2198-4182 ; |