Robust Computer Vision: Theory and Applications

✍ Scribed by Nicu Sebe, Michael S. Lew (auth.)

• Publisher: Springer Netherlands
• Year: 2003
• Tongue: English
• Leaves: 226
• Series: Computational Imaging and Vision 26
• Edition: 1
• Category: Library

✦ Synopsis

From the foreword by Thomas Huang:
"During the past decade, researchers in computer vision have found that probabilistic machine learning methods are extremely powerful. This book describes some of these methods. In addition to the Maximum Likelihood framework, Bayesian Networks, and Hidden Markov models are also used. Three aspects are stressed: features, similarity metric, and models. Many interesting and important new results, based on research by the authors and their collaborators, are presented.

Although this book contains many new results, it is written in a style that suits both experts and novices in computer vision."

✦ Table of Contents

Front Matter....Pages i-xv
Introduction....Pages 1-23
Maximum Likelihood Framework....Pages 25-59
Color Based Retrieval....Pages 61-82
Robust Texture Analysis....Pages 83-110
Shape Based Retrieval....Pages 111-134
Robust Stereo Matching and Motion Tracking....Pages 135-162
Facial Expression Recognition....Pages 163-197
Back Matter....Pages 199-215

✦ Subjects

Computer Imaging, Vision, Pattern Recognition and Graphics; Artificial Intelligence (incl. Robotics); Data Structures, Cryptology and Information Theory; Multimedia Information Systems; Information Storage and Retrieval