Ebook: Decision Forests for Computer Vision and Medical 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. 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.

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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. A number of exercises encourage the reader to practice their skills with the aid of the provided free software library. An international selection of leading researchers from both academia and industry then contribute their own perspectives on the use of decision forests in real-world applications such as pedestrian tracking, human body pose estimation, pixel-wise semantic segmentation of images and videos, automatic parsing of medical 3D scans, and detection of tumors. The book concludes with a detailed discussion on the efficient implementation of decision forests.

Topics and features:

• With a foreword by Prof. Yali Amit and Prof. Donald 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

With its clear, tutorial structure and supporting exercises, this text will be of great value to students wishing to learn the basics of decision forests, researchers wanting to become more familiar with forest-based learning, 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.

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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. A number of exercises encourage the reader to practice their skills with the aid of the provided free software library. An international selection of leading researchers from both academia and industry then contribute their own perspectives on the use of decision forests in real-world applications such as pedestrian tracking, human body pose estimation, pixel-wise semantic segmentation of images and videos, automatic parsing of medical 3D scans, and detection of tumors. The book concludes with a detailed discussion on the efficient implementation of decision forests.

Topics and features:

• With a foreword by Prof. Yali Amit and Prof. Donald 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

With its clear, tutorial structure and supporting exercises, this text will be of great value to students wishing to learn the basics of decision forests, researchers wanting to become more familiar with forest-based learning, 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.

Content:
Front Matter....Pages I-XIX
Front Matter....Pages 5-5
Introduction: The Abstract Forest Model....Pages 7-23
Classification Forests....Pages 25-45
Regression Forests....Pages 47-58
Density Forests....Pages 59-77
Manifold Forests....Pages 79-93
Semi-supervised Classification Forests....Pages 95-107
Front Matter....Pages 109-109
Keypoint Recognition Using Random Forests and Random Ferns....Pages 111-124
Extremely Randomized Trees and Random Subwindows for Image Classification, Annotation, and Retrieval....Pages 125-141
Class-Specific Hough Forests for Object Detection....Pages 143-157
Hough-Based Tracking of Deformable Objects....Pages 159-173
Efficient Human Pose Estimation from Single Depth Images....Pages 175-192
Anatomy Detection and Localization in 3D Medical Images....Pages 193-209
Semantic Texton Forests for Image Categorization and Segmentation....Pages 211-227
Semi-supervised Video Segmentation Using Decision Forests....Pages 229-244
Classification Forests for Semantic Segmentation of Brain Lesions in Multi-channel MRI....Pages 245-260
Manifold Forests for Multi-modality Classification of Alzheimer’s Disease....Pages 261-272
Entanglement and Differentiable Information Gain Maximization....Pages 273-293
Decision Tree Fields: An Efficient Non-parametric Random Field Model for Image Labeling....Pages 295-309
Overview and Scope....Pages 1-2
Notation and Terminology....Pages 3-4
Front Matter....Pages 311-311
Efficient Implementation of Decision Forests....Pages 313-332
The Sherwood Software Library....Pages 333-342
Conclusions....Pages 343-345
Back Matter....Pages 347-368

---

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. A number of exercises encourage the reader to practice their skills with the aid of the provided free software library. An international selection of leading researchers from both academia and industry then contribute their own perspectives on the use of decision forests in real-world applications such as pedestrian tracking, human body pose estimation, pixel-wise semantic segmentation of images and videos, automatic parsing of medical 3D scans, and detection of tumors. The book concludes with a detailed discussion on the efficient implementation of decision forests.

Topics and features:

• With a foreword by Prof. Yali Amit and Prof. Donald 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

With its clear, tutorial structure and supporting exercises, this text will be of great value to students wishing to learn the basics of decision forests, researchers wanting to become more familiar with forest-based learning, 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.

Content:
Front Matter....Pages I-XIX
Front Matter....Pages 5-5
Introduction: The Abstract Forest Model....Pages 7-23
Classification Forests....Pages 25-45
Regression Forests....Pages 47-58
Density Forests....Pages 59-77
Manifold Forests....Pages 79-93
Semi-supervised Classification Forests....Pages 95-107
Front Matter....Pages 109-109
Keypoint Recognition Using Random Forests and Random Ferns....Pages 111-124
Extremely Randomized Trees and Random Subwindows for Image Classification, Annotation, and Retrieval....Pages 125-141
Class-Specific Hough Forests for Object Detection....Pages 143-157
Hough-Based Tracking of Deformable Objects....Pages 159-173
Efficient Human Pose Estimation from Single Depth Images....Pages 175-192
Anatomy Detection and Localization in 3D Medical Images....Pages 193-209
Semantic Texton Forests for Image Categorization and Segmentation....Pages 211-227
Semi-supervised Video Segmentation Using Decision Forests....Pages 229-244
Classification Forests for Semantic Segmentation of Brain Lesions in Multi-channel MRI....Pages 245-260
Manifold Forests for Multi-modality Classification of Alzheimer’s Disease....Pages 261-272
Entanglement and Differentiable Information Gain Maximization....Pages 273-293
Decision Tree Fields: An Efficient Non-parametric Random Field Model for Image Labeling....Pages 295-309
Overview and Scope....Pages 1-2
Notation and Terminology....Pages 3-4
Front Matter....Pages 311-311
Efficient Implementation of Decision Forests....Pages 313-332
The Sherwood Software Library....Pages 333-342
Conclusions....Pages 343-345
Back Matter....Pages 347-368
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