H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 58 Citations 19,378 167 World Ranking 1737 National Ranking 10

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Algorithm
  • Computer vision

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.

His most cited work include:

  • A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging (3010 citations)
  • A duality based approach for realtime TV-L 1 optical flow (1155 citations)
  • Total Generalized Variation (992 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Artificial intelligence (54.85%)
  • Algorithm (44.78%)
  • Computer vision (26.87%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (54.85%)
  • Algorithm (44.78%)
  • Deep learning (17.91%)

In recent papers he was focusing on the following fields of study:

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

  • Compressed sensing which connect with Graphics, Filter, Gradient descent and Computed tomography,
  • Range and related Level of detail,
  • Transfer of learning together with Residual. His study explores the link between Pattern recognition and topics such as Synthetic data that cross with problems in Identification.

Between 2017 and 2021, his most popular works were:

  • Learning a variational network for reconstruction of accelerated MRI data. (549 citations)
  • Learning a variational network for reconstruction of accelerated MRI data. (549 citations)
  • Assessment of the generalization of learned image reconstruction and the potential for transfer learning (79 citations)

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.

Top Publications

A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging

Antonin Chambolle;Thomas Pock.
Journal of Mathematical Imaging and Vision (2011)

3841 Citations

A duality based approach for realtime TV-L 1 optical flow

C. Zach;T. Pock;H. Bischof.
dagm conference on pattern recognition (2007)

1430 Citations

Total Generalized Variation

Kristian Bredies;Karl Kunisch;Thomas Pock.
Siam Journal on Imaging Sciences (2010)

1316 Citations

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)

724 Citations

Learning a variational network for reconstruction of accelerated MRI data.

Kerstin Hammernik;Teresa Klatzer;Erich Kobler;Michael P. Recht.
Magnetic Resonance in Medicine (2018)

629 Citations

Second order total generalized variation (TGV) for MRI

Florian Knoll;Kristian Bredies;Thomas Pock;Rudolf Stollberger.
Magnetic Resonance in Medicine (2011)

499 Citations

Anisotropic Huber-L1 Optical Flow

Manuel Werlberger;Werner Trobin;Thomas Pock;Andreas Wedel.
british machine vision conference (2009)

487 Citations

PROST: Parallel robust online simple tracking

Jakob Santner;Christian Leistner;Amir Saffari;Thomas Pock.
computer vision and pattern recognition (2010)

474 Citations

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)

472 Citations

Diagonal preconditioning for first order primal-dual algorithms in convex optimization

Thomas Pock;Antonin Chambolle.
international conference on computer vision (2011)

436 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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Contact us

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