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)
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