Decision Forests for Computer Vision and Medical Image Analysis

Author:   Antonio Criminisi ,  J Shotton
Publisher:   Springer London Ltd
Edition:   2013 ed.
ISBN:  

9781447149286


Pages:   368
Publication Date:   07 February 2013
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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Decision Forests for Computer Vision and Medical Image Analysis


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Full Product Details

Author:   Antonio Criminisi ,  J Shotton
Publisher:   Springer London Ltd
Imprint:   Springer London Ltd
Edition:   2013 ed.
Dimensions:   Width: 15.50cm , Height: 2.50cm , Length: 23.50cm
Weight:   7.806kg
ISBN:  

9781447149286


ISBN 10:   1447149289
Pages:   368
Publication Date:   07 February 2013
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
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)


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