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Decision Forests for Computer Vision and Medical Image Analysis

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Cover of 'Decision Forests for Computer Vision and Medical Image Analysis'

Table of Contents

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    Book Overview
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    Chapter 1 Overview and Scope
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    Chapter 2 Notation and Terminology
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    Chapter 3 Introduction: The Abstract Forest Model
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    Chapter 4 Classification Forests
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    Chapter 5 Regression Forests
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    Chapter 6 Density Forests
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    Chapter 7 Manifold Forests
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    Chapter 8 Semi-supervised Classification Forests
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    Chapter 9 Keypoint Recognition Using Random Forests and Random Ferns
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    Chapter 10 Extremely Randomized Trees and Random Subwindows for Image Classification, Annotation, and Retrieval
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    Chapter 11 Class-Specific Hough Forests for Object Detection
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    Chapter 12 Hough-Based Tracking of Deformable Objects
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    Chapter 13 Efficient Human Pose Estimation from Single Depth Images
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    Chapter 14 Anatomy Detection and Localization in 3D Medical Images
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    Chapter 15 Semantic Texton Forests for Image Categorization and Segmentation
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    Chapter 16 Semi-supervised Video Segmentation Using Decision Forests
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    Chapter 17 Classification Forests for Semantic Segmentation of Brain Lesions in Multi-channel MRI
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    Chapter 18 Manifold Forests for Multi-modality Classification of Alzheimer’s Disease
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    Chapter 19 Entanglement and Differentiable Information Gain Maximization
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    Chapter 20 Decision Tree Fields: An Efficient Non-parametric Random Field Model for Image Labeling
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    Chapter 21 Efficient Implementation of Decision Forests
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    Chapter 22 The Sherwood Software Library
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    Chapter 23 Conclusions
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