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

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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.

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Detail Information

Series Title

Studies in Systems, Decision and Control

Call Number

-

Publisher

Cham, Switzerland : Springer., 2015

Collation

XV, 199

Language

English

ISBN/ISSN

978-3-319-11324-1

Classification

NONE

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

1

Subject(s)

Robotics
Computer Vision
Automated Pattern Recognition
Computational Intelligence
Control
Automation

Specific Detail Info

-

Statement of Responsibility

Jens Spehr

Other Information

Cataloger

sara

Source

-

Validator

Taufik

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