Overview

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 ina hands-on manner.

Full Product Details

Author:   Antonio Criminisi ,  J Shotton ,  Antonio Criminisi
Publisher:   Springer London Ltd
Imprint:   Springer London Ltd
Edition:   Softcover reprint of the original 1st ed. 2013
Dimensions:   Width: 15.50cm , Height: 2.10cm , Length: 23.50cm
Weight:   5.913kg
ISBN:  

9781447169628

ISBN 10:   144716962
Pages:   368
Publication Date:   23 August 2016
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Overview and Scope.- Notation and Terminology.- Part I: The Decision Forest Model.- Introduction.- 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.- Extremely Randomized Trees and Random Subwindows for Image Classification, Annotation, and Retrieval.- Class-Specific Hough Forests for Object Detection.- Hough-Based Tracking of Deformable Objects.- Efficient Human Pose Estimation from Single Depth Images.- Anatomy Detection and Localization in 3D Medical Images.- Semantic Texton Forests for Image Categorization and Segmentation.- Semi-Supervised Video Segmentation Using Decision Forests.- Classification Forests for Semantic Segmentation of Brain Lesions in Multi-Channel MRI.- Manifold Forests for Multi-Modality Classification of Alzheimer’s Disease.- Entangled Forests and Differentiable Information Gain Maximization.- Decision Tree Fields.- Part III: Implementation and Conclusion.- Efficient Implementation of Decision Forests.- The Sherwood Software Library.- Conclusions.

Reviews

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)

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)

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