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Robust Computer Vision

Theory and Applications

  • Nicu Sebe
  • Michael S. Lew

Part of the Computational Imaging and Vision book series (CIVI, volume 26)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Nicu Sebe, Michael S. Lew
    Pages 1-23
  3. Nicu Sebe, Michael S. Lew
    Pages 25-59
  4. Nicu Sebe, Michael S. Lew
    Pages 61-82
  5. Nicu Sebe, Michael S. Lew
    Pages 83-110
  6. Nicu Sebe, Michael S. Lew
    Pages 111-134
  7. Nicu Sebe, Michael S. Lew
    Pages 135-162
  8. Nicu Sebe, Michael S. Lew
    Pages 163-197
  9. Back Matter
    Pages 199-215

About this book

Introduction

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."

Keywords

Active contour Bayesian network Hidden Markov Model Stereo Textur algorithms classification cognition computer vision database emotion emotion recognition learning machine learning perception

Authors and affiliations

  • Nicu Sebe
    • 1
  • Michael S. Lew
    • 1
  1. 1.LIACS Media LabLeiden UniversityLeidenThe Netherlands

Bibliographic information

  • DOI https://doi.org/10.1007/978-94-017-0295-9
  • Copyright Information Springer Science+Business Media B.V. 2003
  • Publisher Name Springer, Dordrecht
  • eBook Packages Springer Book Archive
  • Print ISBN 978-90-481-6290-1
  • Online ISBN 978-94-017-0295-9
  • Series Print ISSN 1381-6446
  • Buy this book on publisher's site