Decision forests for computer vision and medical image analysis /
Decision forests (also known as random forests) are an indispensable tool for automatic image analysis. 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....
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| Format: | eBook |
| Language: | English |
| Published: |
London
Springer London Imprint: Springer,
2013.
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| Series: | Advances in Computer Vision and Pattern Recognition,
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| Subjects: | |
| Online Access: | Click here to view the full text content |
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Table of Contents:
- Overview and Scope
- Notation and Terminology
- Part I: The Decision Forest Model
- Introduction
- Classification Forests
- Regression Forests
- Density Forests
- Manifold Forests
- Semi-Supervised Classification Forests
- Part II: Applications in Computer Vision and Medical Image Analysis
- Keypoint Recognition Using Random Forests and Random Ferns
- Extremely Randomized Trees and Random Subwindows for Image Classification, Annotation, and Retrieval
- Class-Specific Hough Forests for Object Detection
- Hough-Based Tracking of Deformable Objects
- Efficient Human Pose Estimation from Single Depth Images
- Anatomy Detection and Localization in 3D Medical Images
- Semantic Texton Forests for Image Categorization and Segmentation
- Semi-Supervised Video Segmentation Using Decision Forests
- Classification Forests for Semantic Segmentation of Brain Lesions in Multi-Channel MRI
- Manifold Forests for Multi-Modality Classification of Alzheimer's Disease
- Entangled Forests and Differentiable Information Gain Maximization
- Decision Tree Fields
- Part III: Implementation and Conclusion
- Efficient Implementation of Decision Forests
- The Sherwood Software Library
- Conclusions.