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Decision Forests for Computer Vision and Medical Image Analysis

By A. Criminisi , J Shotton

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This practical, easy-to-follow book reviews the theoretical underpinnings of decision forests, organizing the existing literature in a new, general-purpose forest model. Includes exercises and experiments; slides, videos and more reside at a companion website.

Full Description

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  • ISBN13: 978-1-4471-4928-6
  • 388 Pages
  • User Level: Science
  • Publication Date: January 30, 2013
  • Available eBook Formats: PDF
Full Description
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 in a hands-on manner.
Table of Contents

Table of Contents

  1. Overview and Scope.
  2. Notation and Terminology.
  3. Part I: The Decision Forest Model.
  4. Introduction.
  5. Classification Forests.
  6. Regression Forests.
  7. Density Forests.
  8. Manifold Forests.
  9. Semi
  10. Supervised Classification Forests.
  11. Part II: Applications in Computer Vision and Medical Image Analysis.
  12. Keypoint Recognition Using Random Forests and Random Ferns.
  13. Extremely Randomized Trees and Random Subwindows for Image Classification, Annotation, and Retrieval.
  14. Class
  15. Specific Hough Forests for Object Detection.
  16. Hough
  17. Based Tracking of Deformable Objects.
  18. Efficient Human Pose Estimation from Single Depth Images.
  19. Anatomy Detection and Localization in 3D Medical Images.
  20. Semantic Texton Forests for Image Categorization and Segmentation.
  21. Semi
  22. Supervised Video Segmentation Using Decision Forests.
  23. Classification Forests for Semantic Segmentation of Brain Lesions in Multi
  24. Channel MRI.
  25. Manifold Forests for Multi
  26. Modality Classification of Alzheimer’s Disease.
  27. Entangled Forests and Differentiable Information Gain Maximization.
  28. Decision Tree Fields.
  29. Part III: Implementation and Conclusion.
  30. Efficient Implementation of Decision Forests.
  31. The Sherwood Software Library.
  32. Conclusions.

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