R
Rui Li
Researcher at King Abdullah University of Science and Technology
Publications - 34
Citations - 1448
Rui Li is an academic researcher from King Abdullah University of Science and Technology. The author has contributed to research in topics: Computer science & Image segmentation. The author has an hindex of 14, co-authored 26 publications receiving 1363 citations. Previous affiliations of Rui Li include Boston University & Mitsubishi Electric Research Laboratories.
Papers
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Journal ArticleDOI
Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge
Keelin Murphy,B. van Ginneken,Joseph M. Reinhardt,Sven Kabus,Kai Ding,Xiang Deng,Kunlin Cao,Kaifang Du,Gary E. Christensen,V. Garcia,Tom Vercauteren,Nicholas Ayache,Olivier Commowick,Grégoire Malandain,Ben Glocker,Nikos Paragios,Nassir Navab,Vladlena Gorbunova,Jon Sporring,M. de Bruijne,Xiao Han,Mattias P. Heinrich,Julia A. Schnabel,Mark Jenkinson,Cristian Lorenz,Marc Modat,Jamie R. McClelland,Sebastien Ourselin,Sascha E. A. Muenzing,Max A. Viergever,Dante De Nigris,D. L. Collins,Tal Arbel,M. Peroni,Rui Li,Gregory C. Sharp,Alexander Schmidt-Richberg,Jan Ehrhardt,René Werner,Dirk Smeets,Dirk Loeckx,Gang Song,Nicholas J. Tustison,Brian B. Avants,James C. Gee,Marius Staring,Stefan Klein,Berend C. Stoel,Martin Urschler,Manuel Werlberger,Jef Vandemeulebroucke,Simon Rit,David Sarrut,Josien P. W. Pluim +53 more
TL;DR: The organization of the challenge, the data and evaluation methods and the outcome of the initial launch with 20 algorithms, which comprised the comprehensive evaluation and comparison of 20 individual algorithms from leading academic and industrial research groups are detailed.
Book ChapterDOI
Monocular tracking of 3d human motion with a coordinated mixture of factor analyzers
TL;DR: Quantitative comparisons show that the formulation produces more accurate 3D pose estimates over time than those that can be obtained via a number of previously-proposed particle filtering based tracking algorithms.
Proceedings ArticleDOI
Simultaneous Learning of Nonlinear Manifold and Dynamical Models for High-dimensional Time Series
TL;DR: The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series by exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process.
Proceedings ArticleDOI
Articulated Pose Estimation in a Learned Smooth Space of Feasible Solutions
TL;DR: A learning based framework is proposed for estimating human body pose from a single image by using Gaussian Process Latent Variable Modelling and the scaled conjugate gradient method to find the best matching pose in the learned space.
Journal ArticleDOI
3D Human Motion Tracking with a Coordinated Mixture of Factor Analyzers
TL;DR: In this article, a globally coordinated mixture of factor analyzers is learned from motion capture data to approximate the low-dimensional manifold so that a lowdimensional state vector can be obtained for efficient and effective Bayesian tracking.