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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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Citations

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Title
Decision Forests for Computer Vision and Medical Image Analysis
Published by
Springer, London, January 2013
DOI 10.1007/978-1-4471-4929-3
ISBNs
978-1-4471-4928-6, 978-1-4471-4929-3
Editors

A. Criminisi, J. Shotton

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 315 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 2%
United Kingdom 5 2%
Switzerland 4 1%
Germany 3 <1%
Canada 3 <1%
Spain 2 <1%
Italy 1 <1%
France 1 <1%
India 1 <1%
Other 6 2%
Unknown 284 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 94 30%
Student > Master 73 23%
Researcher 47 15%
Student > Bachelor 21 7%
Student > Doctoral Student 11 3%
Other 43 14%
Unknown 26 8%
Readers by discipline Count As %
Computer Science 144 46%
Engineering 83 26%
Agricultural and Biological Sciences 13 4%
Medicine and Dentistry 10 3%
Mathematics 5 2%
Other 19 6%
Unknown 41 13%