Criminisi / Shotton Decision Forests for Computer Vision and Medical Image Analysis

2013
ISBN: 978-1-4471-4929-3
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 368 Seiten, eBook

Reihe: Advances in Pattern Recognition

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.

Zielgruppe

Research

Autoren/Hrsg.

Criminisi, Antonio

Shotton, J

Inhaltsverzeichnis

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. Marée, 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 Möller, 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