Decision Forests for Computer Vision and Medical Image Analysis

  • A. Criminisi
  • J. Shotton

Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Table of contents

  1. Front Matter
    Pages I-XIX
  2. A. Criminisi, J. Shotton
    Pages 1-2
  3. A. Criminisi, J. Shotton
    Pages 3-4
  4. The Decision Forest Model

    1. Front Matter
      Pages 5-5
    2. A. Criminisi, J. Shotton
      Pages 7-23
    3. A. Criminisi, J. Shotton
      Pages 25-45
    4. A. Criminisi, J. Shotton
      Pages 47-58
    5. A. Criminisi, J. Shotton
      Pages 59-77
    6. A. Criminisi, J. Shotton
      Pages 79-93
    7. A. Criminisi, J. Shotton
      Pages 95-107
  5. Applications in Computer Vision and Medical Image Analysis

    1. Front Matter
      Pages 109-109
    2. J. Gall, V. Lempitsky
      Pages 143-157
    3. M. Godec, P. M. Roth, H. Bischof
      Pages 159-173
    4. J. Shotton, R. Girshick, A. Fitzgibbon, T. Sharp, M. Cook, M. Finocchio et al.
      Pages 175-192
    5. A. Criminisi, D. Robertson, O. Pauly, B. Glocker, E. Konukoglu, J. Shotton et al.
      Pages 193-209
    6. M. Johnson, J. Shotton, R. Cipolla
      Pages 211-227
    7. V. Badrinarayanan, I. Budvytis, R. Cipolla
      Pages 229-244
    8. E. Geremia, D. Zikic, O. Clatz, B. H. Menze, B. Glocker, E. Konukoglu et al.
      Pages 245-260

About this book

Introduction

Decision forests (also known as random forests) are an indispensable tool for automatic image analysis.

This practical and easy-to-follow text explores the theoretical underpinnings of decision forests, organizing the vast existing literature on the field within a new, general-purpose forest model. A number of exercises encourage the reader to practice their skills with the aid of the provided free software library. An international selection of leading researchers from both academia and industry then contribute their own perspectives on the use of decision forests in real-world applications such as pedestrian tracking, human body pose estimation, pixel-wise semantic segmentation of images and videos, automatic parsing of medical 3D scans, and detection of tumors. The book concludes with a detailed discussion on the efficient implementation of decision forests.

Topics and features:

  • With a foreword by Prof. Yali Amit and Prof. Donald Geman, recounting their participation in the development of decision forests
  • Introduces a flexible decision forest model, capable of addressing a large and diverse set of image and video analysis tasks
  • Investigates both the theoretical foundations and the practical implementation of decision forests
  • Discusses the use of decision forests for such tasks as classification, regression, density estimation, manifold learning, active learning and semi-supervised classification
  • Includes exercises and experiments throughout the text, with solutions, slides, demo videos and other supplementary material provided at an associated website
  • Provides a free, user-friendly software library, enabling the reader to experiment with forests in a hands-on manner

With its clear, tutorial structure and supporting exercises, this text will be of great value to students wishing to learn the basics of decision forests, researchers wanting to become more familiar with forest-based learning, and practitioners interested in exploring modern and efficient image analysis techniques.

Dr. A. Criminisi and Dr. J. Shotton are Senior Researchers in the Computer Vision Group at Microsoft Research Cambridge, UK.

Keywords

Decision Forest Kinect Random Decision Forest Random Forests Randomized Trees

Editors and affiliations

  • A. Criminisi
    • 1
  • J. Shotton
    • 2
  1. 1.Microsoft Research Ltd.CambridgeUnited Kingdom
  2. 2.Microsoft Research Ltd.CambridgeUnited Kingdom

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4471-4929-3
  • Copyright Information Springer-Verlag London 2013
  • Publisher Name Springer, London
  • eBook Packages Computer Science
  • Print ISBN 978-1-4471-4928-6
  • Online ISBN 978-1-4471-4929-3
  • Series Print ISSN 2191-6586
  • Series Online ISSN 2191-6594
  • About this book