05149nam 2200565Ia 450 991043756800332120200520144314.01-299-33614-01-4471-4929-710.1007/978-1-4471-4929-3(OCoLC)827212143(MiFhGG)GVRL6WVZ(CKB)2550000001017934(MiAaPQ)EBC1106191(EXLCZ)99255000000101793420130211d2013 uy 0engurun|---uuuuatxtccrDecision forests for computer vision and medical image analysis /A. Criminisi, J. Shotton, editors1st ed. 2013.London ;New York Springerc20131 online resource (xix, 368 pages) illustrations (some color)Advances in computer vision and pattern recognition"ISSN: 2191-6586."1-4471-4928-9 Includes bibliographical references and index.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.Decision forests (also known as random forests) are an indispensable tool for automatic image analysis. 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. A number of exercises encourage the reader to practice their skills with the aid of the provided free software library. An international selection of leading researchers from both academia and industry then contribute their own perspectives on the use of decision forests in real-world applications such as pedestrian tracking, human body pose estimation, pixel-wise semantic segmentation of images and videos, automatic parsing of medical 3D scans, and detection of tumors. The book concludes with a detailed discussion on the efficient implementation of decision forests. Topics and features: With a foreword by Prof. Yali Amit and Prof. Donald 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 With its clear, tutorial structure and supporting exercises, this text will be of great value to students wishing to learn the basics of decision forests, researchers wanting to become more familiar with forest-based learning, and practitioners interested in exploring modern and efficient image analysis techniques. Dr. A. Criminisi and Dr. J. Shotton are Senior Researchers in the Computer Vision Group at Microsoft Research Cambridge, UK.Advances in computer vision and pattern recognition.Decision treesComputer visionImage processingDigital techniquesDiagnostic imagingDigital techniquesDecision trees.Computer vision.Image processingDigital techniques.Diagnostic imagingDigital techniques.511.52Criminisi Antonio1972-1749912Shotton J1749913MiAaPQMiAaPQMiAaPQBOOK9910437568003321Decision forests for computer vision and medical image analysis4184377UNINA01930nam0 22003973i 450 MIL010431820251003044223.0883175500520010309d1991 ||||0itac50 baitaitaitz01i xxxe z01nz01ncRDAcarrierMedio Occidenteˆuna ‰periferia d'Europa tra politica e trasformazioneFortunata Pisellicon la collaborazione di Manuela AfonsoVeneziaMarsilio1991XII, 296 p.ill.22 cm.Saggi Marsilio. Storia e scienze sociali001CFI01650172001 Saggi Marsilio. Storia e scienze socialiMedio OccidenteIEI0738336DDSV32278064795COIMBRACondizioni economiche e sociali1971-1990FIRNAPC071646I320.946935SITUAZIONE E CONDIZIONI POLITICHE. BEIRA LITORAL <PROV.>21946.9Storia del Portogallo14946.9044STORIA DEL PORTOGALLO, 1974-20Piselli, FortunataRAVV05962507035258Afonso, ManuelaRAVV059626ITIT-00000020010309IT-BN0095 IT-NA0079 IT-NA0070 NAP BNMAGAZZINO magazzini correnti divisi per anniNAP BU4 B Propedeutica. Formato cm. 20,1-28.NAP 01POZZO LIB.Vi sono collocati fondi di economia, periodici di ingegneria e scienze, periodici di economia e statistica e altri fondi comprendenti documenti di economia pervenuti in dono. MIL0104318Biblioteca Centralizzata di Ateneo1 v. 01POZZO LIB.ECON MON 3910 0101 0000113005E VMA 1 v. (Precedente collocazione GUERRAZZI B 1720)B 2009122820091228 01 BN BUMedio Occidente64795UNISANNIO