2022 - Research.com Computer Science in Austria Leader Award
Thomas Pock mostly deals with Algorithm, Artificial intelligence, Computer vision, Mathematical optimization and Optical flow. His Algorithm research integrates issues from Image segmentation, Noise reduction, Reaction–diffusion system and Linear filter. His Artificial intelligence study combines topics in areas such as Machine learning, Graphics and Pattern recognition.
His Computer vision study deals with Regularization intersecting with Critical point. The study incorporates disciplines such as Function, Rate of convergence, Total variation denoising and Prior probability in addition to Mathematical optimization. His research integrates issues of Classification of discontinuities and Robustness in his study of Optical flow.
The scientist’s investigation covers issues in Artificial intelligence, Algorithm, Computer vision, Optical flow and Iterative reconstruction. His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning and Pattern recognition. His biological study spans a wide range of topics, including Image processing, Image restoration and Mathematical optimization.
His Computer vision research is multidisciplinary, relying on both Computer graphics and Graphics. Thomas Pock interconnects Pixel and Robustness in the investigation of issues within Optical flow. His work deals with themes such as Artificial neural network and Image quality, which intersect with Deep learning.
His primary scientific interests are in Artificial intelligence, Algorithm, Deep learning, Iterative reconstruction and Pattern recognition. Artificial intelligence is closely attributed to Computer vision in his work. His Algorithm study integrates concerns from other disciplines, such as Inverse problem and Optimal control.
The concepts of his Deep learning study are interwoven with issues in Artificial neural network, Convolutional neural network, Image quality and Robustness. His Iterative reconstruction study also includes
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging
Antonin Chambolle;Thomas Pock.
Journal of Mathematical Imaging and Vision (2011)
A duality based approach for realtime TV-L 1 optical flow
C. Zach;T. Pock;H. Bischof.
dagm conference on pattern recognition (2007)
Total Generalized Variation
Kristian Bredies;Karl Kunisch;Thomas Pock.
Siam Journal on Imaging Sciences (2010)
Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration
Yunjin Chen;Thomas Pock.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2017)
Learning a variational network for reconstruction of accelerated MRI data.
Kerstin Hammernik;Teresa Klatzer;Erich Kobler;Michael P. Recht.
Magnetic Resonance in Medicine (2018)
Second order total generalized variation (TGV) for MRI
Florian Knoll;Kristian Bredies;Thomas Pock;Rudolf Stollberger.
Magnetic Resonance in Medicine (2011)
An Improved Algorithm for TV-L1 Optical Flow
Andreas Wedel;Thomas Pock;Christopher Zach;Horst Bischof.
Statistical and Geometrical Approaches to Visual Motion Analysis (2009)
Anisotropic Huber-L1 Optical Flow
Manuel Werlberger;Werner Trobin;Thomas Pock;Andreas Wedel.
british machine vision conference (2009)
PROST: Parallel robust online simple tracking
Jakob Santner;Christian Leistner;Amir Saffari;Thomas Pock.
computer vision and pattern recognition (2010)
An introduction to Total Variation for Image Analysis
Antonin Chambolle;Vicent Caselles;Matteo Novaga;Daniel Cremers.
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