Decision Forests for Computer Vision and Medical Image Analysis - Rilegato

Rilegato

ISBN 10: 1447149289 ISBN 13: 9781447149286

Casa editrice: Springer-Nature New York Inc, 2013

Riassunto

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.

Su questo libro

Recensione:

From the reviews:

“This book is a comprehensive presentation of the theory and use of decision forests in a wide range of applications, centered on computer vision and medical imaging. The book is strikingly well integrated. ... This is an excellent volume on the concept, theory, and application of decision forests. ... I highly recommend it to those currently working in the field, as well as researchers desiring an introduction to the application of random forests for imaging applications.” (Creed Jones, Computing Reviews, March, 2014)

Contenuti:

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

Su questa edizione

• Editore: Springer-Nature New York Inc
• Data di pubblicazione: 2013
• ISBN 10: 1447149289
• ISBN 13: 9781447149286
• Rilegatura: Copertina rigida
• Numero di pagine: 368
• Redattore: Criminisi A., Shotton J.