Decision forests for computer vision and medical image analysis

著者

    • Criminisi, Antonio
    • Shotton, J.

書誌事項

Decision forests for computer vision and medical image analysis

A. Criminisi, J. Shotton, editors

（Advances in computer vision and pattern recognition / Sameer Singh, Sing Bing Kang, series editors）

Springer, c2013

  • : [hardback]

大学図書館所蔵 件 / 全7件

この図書・雑誌をさがす

注記

Includes bibliographical references (p.347-365) and index

内容説明・目次

内容説明

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.

目次

Overview and Scope Notation and Terminology Part I: The Decision Forest Model Introduction: The Abstract Forest Model 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 V. Lepetit and P. Fua Extremely Randomized Trees and Random Subwindows for Image Classification, Annotation, and Retrieval R. Maree, L. Wehenkel and P. Geurts Class-Specific Hough Forests for Object Detection J. Gall and V. Lempitsky Hough-Based Tracking of Deformable Objects M. Godec, P. M. Roth and H. Bischof Efficient Human Pose Estimation from Single Depth Images J. Shotton, R. Girshick, A. Fitzgibbon, T. Sharp, M. Cook, M. Finocchio, R. Moore, P. Kohli, A. Criminisi, A. Kipman and A. Blake Anatomy Detection and Localization in 3D Medical Images A. Criminisi, D. Robertson, O. Pauly, B. Glocker, E. Konukoglu, J. Shotton, D. Mateus, A. Martinez Moeller, S. G. Nekolla and N. Navab Semantic Texton Forests for Image Categorization and Segmentation M. Johnson, J. Shotton and R. Cipolla Semi-Supervised Video Segmentation Using Decision Forests V. Badrinarayanan, I. Budvytis and R. Cipolla Classification Forests for Semantic Segmentation of Brain Lesions in Multi-Channel MRI E. Geremia, D. Zikic, O. Clatz, B. H. Menze, B. Glocker, E. Konukoglu, J. Shotton, O. M. Thomas, S. J. Price, T. Das, R. Jena, N. Ayache and A. Criminisi Manifold Forests for Multi-Modality Classification of Alzheimer's Disease K. R. Gray, P. Aljabar, R. A. Heckemann, A. Hammers and D. Rueckert Entangled Forests and Differentiable Information Gain Maximization A. Montillo, J. Tu, J. Shotton, J. Winn, J. E. Iglesias, D. N. Metaxas, and A. Criminisi Decision Tree Fields: An Efficient Non-Parametric Random Field Model for Image Labeling S. Nowozin, C. Rother, S. Bagon, T. Sharp, B. Yao and P. Kohli Part III: Implementation and Conclusion Efficient Implementation of Decision Forests J. Shotton, D. Robertson and T. Sharp The Sherwood Software Library D. Robertson, J. Shotton and T. Sharp Conclusions

「Nielsen BookData」 より

関連文献： 1件中  1-1を表示

詳細情報

ページトップへ