Decision forests for computer vision and medical image analysis
Author(s)
Bibliographic Information
Decision forests for computer vision and medical image analysis
（Advances in computer vision and pattern recognition / Sameer Singh, Sing Bing Kang, series editors）
Springer, c2013
- : [hardback]
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Note
Includes bibliographical references (p.347-365) and index
Description and Table of Contents
Description
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.
Table of Contents
Overview and Scope
Notation and Terminology
Part I: The Decision Forest Model
Introduction: The Abstract Forest Model
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
V. Lepetit and P. Fua
Extremely Randomized Trees and Random Subwindows for Image Classification, Annotation, and Retrieval
R. Maree, L. Wehenkel and P. Geurts
Class-Specific Hough Forests for Object Detection
J. Gall and V. Lempitsky
Hough-Based Tracking of Deformable Objects
M. Godec, P. M. Roth and H. Bischof
Efficient Human Pose Estimation from Single Depth Images
J. Shotton, R. Girshick, A. Fitzgibbon, T. Sharp, M. Cook, M. Finocchio, R. Moore, P. Kohli, A. Criminisi, A. Kipman and A. Blake
Anatomy Detection and Localization in 3D Medical Images
A. Criminisi, D. Robertson, O. Pauly, B. Glocker, E. Konukoglu, J. Shotton, D. Mateus, A. Martinez Moeller, S. G. Nekolla and N. Navab
Semantic Texton Forests for Image Categorization and Segmentation
M. Johnson, J. Shotton and R. Cipolla
Semi-Supervised Video Segmentation Using Decision Forests
V. Badrinarayanan, I. Budvytis and R. Cipolla
Classification Forests for Semantic Segmentation of Brain Lesions in Multi-Channel MRI
E. Geremia, D. Zikic, O. Clatz, B. H. Menze, B. Glocker, E. Konukoglu, J. Shotton, O. M. Thomas, S. J. Price, T. Das, R. Jena, N. Ayache and A. Criminisi
Manifold Forests for Multi-Modality Classification of Alzheimer's Disease
K. R. Gray, P. Aljabar, R. A. Heckemann, A. Hammers and D. Rueckert
Entangled Forests and Differentiable Information Gain Maximization
A. Montillo, J. Tu, J. Shotton, J. Winn, J. E. Iglesias, D. N. Metaxas, and A. Criminisi
Decision Tree Fields: An Efficient Non-Parametric Random Field Model for Image Labeling
S. Nowozin, C. Rother, S. Bagon, T. Sharp, B. Yao and P. Kohli
Part III: Implementation and Conclusion
Efficient Implementation of Decision Forests
J. Shotton, D. Robertson and T. Sharp
The Sherwood Software Library
D. Robertson, J. Shotton and T. Sharp
Conclusions
by "Nielsen BookData"
