Decision Forests for Computer Vision and Medical Image Analysis

1. Front Matter

   Pages I-XIX

   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

      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

      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

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.

• Decision Forest
• Kinect
• Random Decision Forest
• Random Forests
• Randomized Trees

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)

• Microsoft Research Ltd., Cambridge, United Kingdom

  A. Criminisi, J. Shotton

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