2020 - Fellow of the Indian National Academy of Engineering (INAE)
2016 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to computer vision and pattern recognition
2014 - IEEE Fellow For contributions to subspace clustering and motion segmentation in computer vision
2012 - IAPR J. K. Aggarwal Prize "For outstanding contributions to generalized principal component analysis (GPCA) and subspace clustering in computer vision and pattern recognition."
2009 - Fellow of Alfred P. Sloan Foundation
His main research concerns Artificial intelligence, Pattern recognition, Cluster analysis, Computer vision and Linear subspace. His Artificial intelligence research incorporates elements of Algorithm and Affine transformation. His studies in Pattern recognition integrate themes in fields like Singular value, Spectral clustering and Singular value decomposition.
His Cluster analysis research includes elements of Identification, Theoretical computer science and Dimensionality reduction. His biological study spans a wide range of topics, including Linear dynamical system and Benchmark. René Vidal interconnects Data point and Sparse matrix in the investigation of issues within Linear subspace.
René Vidal focuses on Artificial intelligence, Computer vision, Algorithm, Pattern recognition and Cluster analysis. His work on Artificial intelligence is being expanded to include thematically relevant topics such as Machine learning. The Computer vision study combines topics in areas such as Linear dynamical system and Affine transformation.
His study in Algorithm is interdisciplinary in nature, drawing from both Dimension, Matrix and Mathematical optimization. The Pattern recognition study combines topics in areas such as 3D pose estimation and Pose. René Vidal works mostly in the field of Cluster analysis, limiting it down to topics relating to Linear subspace and, in certain cases, Subspace topology, Data point, Synthetic data and Polynomial.
His primary areas of study are Artificial intelligence, Algorithm, Applied mathematics, Linear subspace and Cluster analysis. His Artificial intelligence research includes themes of Machine learning, Computer vision and Pattern recognition. In general Algorithm study, his work on Regularization often relates to the realm of Diffusion MRI, thereby connecting several areas of interest.
His Applied mathematics research incorporates elements of Dynamical systems theory, Matrix decomposition, Gradient descent, Differential equation and Discretization. His Linear subspace research incorporates themes from Data point and Subspace topology. He usually deals with Cluster analysis and limits it to topics linked to Dimension and Embedding.
René Vidal mainly investigates Applied mathematics, Optimization problem, Differential equation, Algorithm and Dynamical systems theory. His Applied mathematics research is multidisciplinary, incorporating perspectives in Discretization, Matrix, Subgradient method and Outlier. His study focuses on the intersection of Subgradient method and fields such as Grassmannian with connections in the field of Subspace topology.
His Optimization problem study combines topics from a wide range of disciplines, such as Imaging science, Regularization, Blind deconvolution and Signal processing. The study incorporates disciplines such as Matrix norm, Linear combination, Regular polygon, Matrix decomposition and Principal component analysis in addition to Algorithm. His Dynamical systems theory study incorporates themes from Symplectic geometry and Hamiltonian system.
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Sparse Subspace Clustering: Algorithm, Theory, and Applications
E. Elhamifar;R. Vidal.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2013)
Sparse subspace clustering
Ehsan Elhamifar;Rene Vidal.
computer vision and pattern recognition (2009)
Generalized principal component analysis (GPCA)
R. Vidal;Yi Ma;S. Sastry.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2005)
IEEE Signal Processing Magazine (2011)
Histograms of oriented optical flow and Binet-Cauchy kernels on nonlinear dynamical systems for the recognition of human actions
Rizwan Chaudhry;Avinash Ravichandran;Gregory Hager;Rene Vidal.
computer vision and pattern recognition (2009)
A Benchmark for the Comparison of 3-D Motion Segmentation Algorithms
R. Tron;R. Vidal.
computer vision and pattern recognition (2007)
Principal Component Analysis
René Vidal;Yi Ma;S. Shankar Sastry.
Temporal Convolutional Networks for Action Segmentation and Detection
Colin Lea;Michael D. Flynn;Rene Vidal;Austin Reiter.
computer vision and pattern recognition (2017)
Probabilistic pursuit-evasion games: theory, implementation, and experimental evaluation
R. Vidal;O. Shakernia;H.J. Kim;D.H. Shim.
international conference on robotics and automation (2002)
Berkeley MHAD: A comprehensive Multimodal Human Action Database
F. Ofli;R. Chaudhry;G. Kurillo;R. Vidal.
workshop on applications of computer vision (2013)
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