Decision Forests for Computer Vision and Medical Image Analysis

Editors:

ISBN: 978-1-4471-4928-6 (Print) 978-1-4471-4929-3 (Online)

Table of contents (23 chapters)

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  1. Front Matter

    Pages I-XIX

  2. No Access

    Chapter

    Pages 1-2

    Overview and Scope

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    Chapter

    Pages 3-4

    Notation and Terminology

  4. The Decision Forest Model

    1. Front Matter

      Pages 5-5

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      Chapter

      Pages 7-23

      Introduction: The Abstract Forest Model

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      Chapter

      Pages 25-45

      Classification Forests

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      Chapter

      Pages 47-58

      Regression Forests

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      Chapter

      Pages 59-77

      Density Forests

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      Chapter

      Pages 79-93

      Manifold Forests

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      Chapter

      Pages 95-107

      Semi-supervised Classification Forests

  5. Applications in Computer Vision and Medical Image Analysis

    1. Front Matter

      Pages 109-109

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      Chapter

      Pages 111-124

      Keypoint Recognition Using Random Forests and Random Ferns

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      Chapter

      Pages 125-141

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

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      Chapter

      Pages 143-157

      Class-Specific Hough Forests for Object Detection

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      Chapter

      Pages 159-173

      Hough-Based Tracking of Deformable Objects

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      Chapter

      Pages 175-192

      Efficient Human Pose Estimation from Single Depth Images

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      Chapter

      Pages 193-209

      Anatomy Detection and Localization in 3D Medical Images

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      Chapter

      Pages 211-227

      Semantic Texton Forests for Image Categorization and Segmentation

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      Chapter

      Pages 229-244

      Semi-supervised Video Segmentation Using Decision Forests

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      Chapter

      Pages 245-260

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

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      Chapter

      Pages 261-272

      Manifold Forests for Multi-modality Classification of Alzheimer’s Disease

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      Chapter

      Pages 273-293

      Entanglement and Differentiable Information Gain Maximization

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      Chapter

      Pages 295-309

      Decision Tree Fields: An Efficient Non-parametric Random Field Model for Image Labeling

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