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" Decision forests for computer vision and medical image analysis "
A. Criminisi, J. Shotton, editors
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BL
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| Record Number
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666068
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dltt
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| Title & Author
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Decision forests for computer vision and medical image analysis\ A. Criminisi, J. Shotton, editors
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| Publication Statement
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London ;New York :: Springer,, c2013
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| Series Statement
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Advances in Computer Vision and Pattern Recognition
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| Page. NO
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1 online resource (366 p.)
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| ISBN
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9781447149293 (electronic bk.)
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: 1447149297 (electronic bk.)
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9781447149286
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| Bibliographies/Indexes
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Includes bibliographical references and index
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| Contents
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Chapter 1: Overview and scope -- Chapter 2: Notation and terminology -- Chapter 3: Introduction: the abstract forest model -- Chapter 4: Classification forests -- Chapter 5: Regression forests -- Chapter 6: Density forests -- Chapter 7: Manifold forests -- Chapter 8: Semi-supervised classification forests -- Chapter 9: Keypoint recognition using random forests and random ferns -- Chapter 10: Extremely randomized trees and random subwindows for image classification, annotation, and retrieval -- Chapter 11: Class-specific hough forests for object detection -- Chapter 12: Hough-based tracking of deformable objects -- Chapter 13: Efficient human pose estimation from single depth images -- Chapter 14: Anatomy detection and localization in 3D medical images -- Chapter 15: Semantic texton forests for image categorization and segmentation -- Chapter 16: Semi-supervised video segmentation using decision forests -- Chapter 17: Classification forestts for semantic segmentation of brain lesions in multi-channel MRI -- Chapter 18: Manifold forests for multi-modality classification of Alzheimer's Disease -- Chapter 19: Entanglement and differentiable information gain maximization -- Chapter 20: Decision tree fields: an efficient non-parametric random field model for image labeling -- Chapter 21: Efficient implementation of decision forests -- Chapter 22: The Sherwood Software Library -- Chapter 23: Conclusions
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| Abstract
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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
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| Subject
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Decision trees
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| Subject
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Computer vision
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| Subject
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Image processing-- Digital techniques
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Diagnostic imaging-- Digital techniques
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| Dewey Classification
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511.52
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| LC Classification
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QA166.2
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QA166.2
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| Added Entry
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Criminisi, Antonio,1972-
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Shotton, J
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Ohio Library and Information Network
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