Decision Forests for Computer Vision and Medical Image Analysis
and practitioners interested in exploring modern and efficient image analysis techniques. Dr. A. Criminisi and Dr. J. Shotton are Senior Researchers in the Computer Vision Group at Microsoft Research Cambridge, UK.
| Other Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
London
Springer London
2013, 2013
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| Edition: | 1st ed. 2013 |
| Series: | Advances in Computer Vision and Pattern Recognition
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
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