ENGLISH

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

By Jens Spehr (auth.)

Book information

Publisher
Springer International Publishing
Year
2015
ISBN
978-3-319-11324-1, 978-3-319-11325-8
DOI
10.1007/978-3-319-11325-8
Language
english
Format
PDF
Filesize
21 MB (21555585 bytes)
Series
Studies in Systems, Decision and Control 11
Edition
1
Pages
199\210
Time added
2015-02-17 02:00:00
Robotics and Automation; Computational Intelligenc

Description

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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