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

✍ Scribed by Jens Spehr (auth.)

• Publisher: Springer International Publishing
• Year: 2015
• Tongue: English
• Leaves: 210
• Series: Studies in Systems, Decision and Control 11
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages 1-14
Introduction....Pages 1-6
Probabilistic Graphical Models....Pages 7-20
Hierarchical Graphical Models....Pages 21-65
Learning of Hierarchical Models....Pages 67-83
Object Recognition....Pages 85-120
Human Pose Estimation....Pages 121-133
Human Behavior Analysis....Pages 135-159
Scene Understanding for Intelligent Vehicles....Pages 161-176
Conclusion....Pages 177-184
Back Matter....Pages 185-199

✦ Subjects

Robotics and Automation; Computational Intelligence; Image Processing and Computer Vision; Pattern Recognition