eBook Decision Forests for Computer Vision and Medical Image Analysis, 1st Edition

  • Published By:
  • ISBN-10: 1447149297
  • ISBN-13: 9781447149293
  • DDC: 511.52
  • Grade Level Range: College Freshman - College Senior
  • 368 Pages | eBook
  • Original Copyright 2013 | Published/Released June 2014
  • This publication's content originally published in print form: 2013
  • Price:  Sign in for price



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

Front Cover.
Half Title Page.
Other Frontmatter.
Title Page.
Copyright Page.
1: Overview and Scope.
2: Notation and Terminology.
3: The Decision Forest Model.
4: Introduction: The Abstract Forest Model.
5: Classification Forests.
6: Regression Forests.
7: Density Forests.
8: Manifold Forests.
9: Semi-Supervised Classification Forests.
10: Applications in Computer Vision and Medical Image Analysis.
11: Keypoint Recognition Using Random Forests and Random Ferns.
12: Extremely Randomized Trees and Random Subwindows for Image Classification, Annotation, and Retrieval.
13: Class-Specific Hough Forests for Object Detection.
14: Hough-Based Tracking of Deformable Objects.
15: Efficient Human Pose Estimation from Single Depth Images.
16: Anatomy Detection and Localization in 3d Medical Images.
17: Semantic Texton Forests for Image Categorization and Segmentation.
18: Semi-Supervised Video Segmentation Using Decision Forests.
19: Classification Forests for Semantic Segmentation of Brain Lesions in Multi-Channel MRI.
20: Manifold Forests for Multi-Modality Classification of Alzheimer's Disease.
21: Entanglement and Differentiable Information Gain Maximization.
22: Decision Tree Fields: An Efficient Non-Parametric Random Field Model for Image Labeling.
23: Implementation and Conclusion.
24: Efficient Implementation of Decision Forests.
25: The Sherwood Software Library.
26: Conclusions.