Tanja Teuber

Computer Vision and Pattern Recognition · 2010–2011

A researcher in the Science corpus: 3 papers; trusts 38 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 42 citations into it; #7,685 of 21,575 by trust here, #15,529 by citations
• Computational Mechanics — 29 citations into it; #2,423 of 10,001 by trust here, #5,350 by citations
• Mathematical Physics — 11 citations into it; #2,577 of 8,562 by trust here, #3,828 by citations
• Computational Theory and Mathematics — 8 citations into it; #9,981 of 12,182 by trust here, #11,462 by citations
• Radiology, Nuclear Medicine and Imaging — 8 citations into it
• Biomedical Engineering — 7 citations into it
• Numerical Methods & Scientific Computing — 7 citations into it

Whom Tanja Teuber trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Antonin Chambolle
2. Stanley J. Osher
3. Yurii E. Nesterov
4. Patrick L. Combettes
5. Amir Beck
6. Tom Goldstein
7. Guy Gilboa
8. Imre Csiszár
9. Pierre-Louis Lions
10. Yilun Wang

Who trusts Tanja Teuber overall

1. Patrick L. Combettes
2. Jean-Christophe Pesquet
3. Antonin Chambolle
4. Fabrizio Argenti
5. Konstantinos Papafitsoros
6. Nikos Komodakis
7. Thomas Pock

Papers from Tanja Teuber's perspective

Ordered by how strongly the researchers Tanja Teuber trusts endorse each paper. Same corpus, a different order for every researcher.

1. Nonlinear total variation based noise removal algorithms
2. An Algorithm for Total Variation Minimization and Applications (Journal of Mathematical Imaging and Vision)
3. ImageNet classification with deep convolutional neural networks
4. Regression Shrinkage and Selection via the Lasso
5. The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming
6. Splitting Algorithms for the Sum of Two Nonlinear Operators
7. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. The Split Bregman Method for L1-Regularized Problems
10. Total Variation Minimization and a Class of Binary MRF Models (Energy Minimization Methods in Computer Vision and Pattern Recognition)

Their papers

• Infimal convolution regularizations with discrete ℓ1-type functionals (2011) — 118 citations
• Removing Multiplicative Noise by Douglas-Rachford Splitting Methods (2010) — 295 citations
• Deblurring Poissonian images by split Bregman techniques (2010) — 353 citations