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Decision Forests for Computer Vision and Medical Image Analysis

ACVPR: DCSN FORESTS FOR CMPTR VISION & MDCL IMAGE ANLYS

Information Technology

Computing & Information Technology

Gt Product Group

Binding Format

Marketing Imprint Name

B2B GT

CL Carton Count

Imprint Name

B2B & B2C

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.

COLFR

POST SECONDARY FOUR YEAR

COLSO

COLJR

COLSR

Duration

Duration Unit Of Measure

Transaction Type

Front Cover.
Half Title Page.
Other Frontmatter.
Title Page.
Copyright Page.
Dedication.
Foreword.
Preface.
Acknowledgements.
Contents.
Contributors.
1: Overview and Scope.
2: Notation and Terminology.
3: The Decision Forest Model.
4: Introduction: The Abstract Forest Model.
5: Classification Forests.
6: Regression Forests.
7: Density Forests.
8: Manifold Forests.
9: Semi-Supervised Classification Forests.
10: Applications in Computer Vision and Medical Image Analysis.
11: Keypoint Recognition Using Random Forests and Random Ferns.
12: Extremely Randomized Trees and Random Subwindows for Image Classification, Annotation, and Retrieval.
13: Class-Specific Hough Forests for Object Detection.
14: Hough-Based Tracking of Deformable Objects.
15: Efficient Human Pose Estimation from Single Depth Images.
16: Anatomy Detection and Localization in 3d Medical Images.
17: Semantic Texton Forests for Image Categorization and Segmentation.
18: Semi-Supervised Video Segmentation Using Decision Forests.
19: Classification Forests for Semantic Segmentation of Brain Lesions in Multi-Channel MRI.
20: Manifold Forests for Multi-Modality Classification of Alzheimer&#x0027;s Disease.
21: Entanglement and Differentiable Information Gain Maximization.
22: Decision Tree Fields: An Efficient Non-Parametric Random Field Model for Image Labeling.
23: Implementation and Conclusion.
24: Efficient Implementation of Decision Forests.
25: The Sherwood Software Library.
26: Conclusions.
References.
Index.

Decision Forests for Computer Vision and Medical Image Analysis.css-1p8rvzy{position:absolute;padding-left:8px;}

Decision Forests for Computer Vision and Medical Image Analysis