BookDOI
Decision Forests for Computer Vision and Medical Image Analysis
Antonio Criminisi,Jamie Shotton +1 more
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TLDR
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.Abstract:
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.read more
Citations
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Journal ArticleDOI
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
Bjoern H. Menze,Andras Jakab,Stefan Bauer,Jayashree Kalpathy-Cramer,Keyvan Farahani,Justin Kirby,Yuliya Burren,N Porz,Johannes Slotboom,Roland Wiest,Levente Lanczi,Elizabeth R. Gerstner,Marc-André Weber,Tal Arbel,Brian B. Avants,Nicholas Ayache,Patricia Buendia,D. Louis Collins,Nicolas Cordier,Jason J. Corso,Antonio Criminisi,Tilak Das,Hervé Delingette,Çağatay Demiralp,Christopher R. Durst,Michel Dojat,Senan Doyle,Joana Festa,Florence Forbes,Ezequiel Geremia,Ben Glocker,Polina Golland,Xiaotao Guo,Andac Hamamci,Khan M. Iftekharuddin,Raj Jena,Nigel M. John,Ender Konukoglu,Danial Lashkari,José Mariz,Raphael Meier,Sérgio Pereira,Doina Precup,Stephen J. Price,Tammy Riklin Raviv,Syed M. S. Reza,Michael Ryan,Duygu Sarikaya,Lawrence H. Schwartz,Hoo-Chang Shin,Jamie Shotton,Carlos A. Silva,Nuno Sousa,Nagesh K. Subbanna,Gábor Székely,Thomas J. Taylor,Owen M. Thomas,Nicholas J. Tustison,Gozde Unal,Flor Vasseur,Max Wintermark,Dong Hye Ye,Liang Zhao,Binsheng Zhao,Darko Zikic,Marcel Prastawa,Mauricio Reyes,Koen Van Leemput +67 more
TL;DR: The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) as mentioned in this paper was organized in conjunction with the MICCAI 2012 and 2013 conferences, and twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low and high grade glioma patients.
Proceedings ArticleDOI
Scene Coordinate Regression Forests for Camera Relocalization in RGB-D Images
Jamie Shotton,Ben Glocker,Christopher Zach,Shahram Izadi,Antonio Criminisi,Andrew Fitzgibbon +5 more
TL;DR: This work addresses the problem of inferring the pose of an RGB-D camera relative to a known 3D scene, given only a single acquired image, and employs a regression forest that is capable of inferting an estimate of each pixel's correspondence to 3D points in the scene's world coordinate frame.
Journal ArticleDOI
Deep convolutional neural networks for multi-modality isointense infant brain image segmentation.
Wenlu Zhang,Rongjian Li,Houtao Deng,Li Wang,Weili Lin,Shuiwang Ji,Dinggang Shen,Dinggang Shen +7 more
TL;DR: This paper proposes to use deep convolutional neural networks (CNNs) for segmenting isointense stage brain tissues using multi-modality MR images, and compared the performance of the approach with that of the commonly used segmentation methods on a set of manually segmented isointENSE stage brain images.
Book ChapterDOI
Learning 6D Object Pose Estimation Using 3D Object Coordinates
TL;DR: This work addresses the problem of estimating the 6D Pose of specific objects from a single RGB-D image by presenting a learned, intermediate representation in form of a dense 3D object coordinate labelling paired with a dense class labelling.
Proceedings ArticleDOI
Fast and accurate image upscaling with super-resolution forests
TL;DR: This paper shows the close relation of previous work on single image super-resolution to locally linear regression and demonstrates how random forests nicely fit into this framework, and proposes to directly map from low to high-resolution patches using random forests.
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI
A simplex method for function minimization
John A. Nelder,R. Mead +1 more
TL;DR: A method is described for the minimization of a function of n variables, which depends on the comparison of function values at the (n 41) vertices of a general simplex, followed by the replacement of the vertex with the highest value by another point.
Journal ArticleDOI
Clinical diagnosis of Alzheimer's disease : report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease
Guy M. McKhann,David A. Drachman,Marshall F. Folstein,Robert Katzman,Donald L. Price,Emanuel M. Stadlan +5 more
TL;DR: The criteria proposed are intended to serve as a guide for the diagnosis of probable, possible, and definite Alzheimer's disease; these criteria will be revised as more definitive information becomes available.
Some methods for classification and analysis of multivariate observations
TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.
Book
C4.5: Programs for Machine Learning
TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.