Skip to main content
SpringerLink
Log in
Book cover

Decision Forests for Computer Vision and Medical Image Analysis

  • Book
  • © 2013

Overview

Editors:
  1. A. Criminisi

    1. Microsoft Research Ltd., Cambridge, United Kingdom

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. J. Shotton

    1. Microsoft Research Ltd., Cambridge, United Kingdom

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • Introduces a flexible decision forest model capable of addressing a large and diverse set of image and video analysis tasks, covering both theoretical foundations and practical implementation
  • 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
  • Includes supplementary material: sn.pub/extras

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

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (23 chapters)

  1. Front Matter

    Pages I-XIX
    Download chapter PDF

  2. Overview and Scope

    • A. Criminisi, J. Shotton
    Pages 1-2

  3. Notation and Terminology

    • A. Criminisi, J. Shotton
    Pages 3-4

  4. The Decision Forest Model

    1. Front Matter

      Pages 5-5
      Download chapter PDF

    2. Introduction: The Abstract Forest Model

      • A. Criminisi, J. Shotton
      Pages 7-23

    3. Classification Forests

      • A. Criminisi, J. Shotton
      Pages 25-45

    4. Regression Forests

      • A. Criminisi, J. Shotton
      Pages 47-58

    5. Density Forests

      • A. Criminisi, J. Shotton
      Pages 59-77

    6. Manifold Forests

      • A. Criminisi, J. Shotton
      Pages 79-93

    7. Semi-supervised Classification Forests

      • A. Criminisi, J. Shotton
      Pages 95-107

  5. Applications in Computer Vision and Medical Image Analysis

    1. Front Matter

      Pages 109-109
      Download chapter PDF

    2. Keypoint Recognition Using Random Forests and Random Ferns

      • V. Lepetit, P. Fua
      Pages 111-124

    3. Extremely Randomized Trees and Random Subwindows for Image Classification, Annotation, and Retrieval

      • R. Marée, L. Wehenkel, P. Geurts
      Pages 125-141

    4. Class-Specific Hough Forests for Object Detection

      • J. Gall, V. Lempitsky
      Pages 143-157

    5. Hough-Based Tracking of Deformable Objects

      • M. Godec, P. M. Roth, H. Bischof
      Pages 159-173

    6. Efficient Human Pose Estimation from Single Depth Images

      • J. Shotton, R. Girshick, A. Fitzgibbon, T. Sharp, M. Cook, M. Finocchio et al.
      Pages 175-192

    7. Anatomy Detection and Localization in 3D Medical Images

      • A. Criminisi, D. Robertson, O. Pauly, B. Glocker, E. Konukoglu, J. Shotton et al.
      Pages 193-209

    8. Semantic Texton Forests for Image Categorization and Segmentation

      • M. Johnson, J. Shotton, R. Cipolla
      Pages 211-227

    9. Semi-supervised Video Segmentation Using Decision Forests

      • V. Badrinarayanan, I. Budvytis, R. Cipolla
      Pages 229-244

    10. Classification Forests for Semantic Segmentation of Brain Lesions in Multi-channel MRI

      • E. Geremia, D. Zikic, O. Clatz, B. H. Menze, B. Glocker, E. Konukoglu et al.
      Pages 245-260
Back to top

Keywords

About this book

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. Topics and features: with a foreword by Prof. Y. Amit and Prof. D. 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 ina hands-on manner.

Reviews

From the reviews:

“This book is a comprehensive presentation of the theory and use of decision forests in a wide range of applications, centered on computer vision and medical imaging. The book is strikingly well integrated. … This is an excellent volume on the concept, theory, and application of decision forests. … I highly recommend it to those currently working in the field, as well as researchers desiring an introduction to the application of random forests for imaging applications.” (Creed Jones, Computing Reviews, March, 2014)

Editors and Affiliations

  • Microsoft Research Ltd., Cambridge, United Kingdom

    A. Criminisi, J. Shotton

Bibliographic Information

  • Book Title: Decision Forests for Computer Vision and Medical Image Analysis

  • Editors: A. Criminisi, J. Shotton

  • Series Title: Advances in Computer Vision and Pattern Recognition

  • DOI: https://doi.org/10.1007/978-1-4471-4929-3

  • Publisher: Springer London

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s) 2013

  • Hardcover ISBN: 978-1-4471-4928-6Published: 07 February 2013

  • Softcover ISBN: 978-1-4471-6962-8Published: 23 August 2016

  • eBook ISBN: 978-1-4471-4929-3Published: 30 January 2013

  • Series ISSN: 2191-6586

  • Series E-ISSN: 2191-6594

  • Edition Number: 1

  • Number of Pages: XIX, 368

  • Topics: Pattern Recognition, Artificial Intelligence

Publish with us

Policies and ethics

Back to top

Search

Navigation