Mikaël Rousson

Computer Vision and Pattern Recognition · 2002–2018

A researcher in the Science corpus: 5 papers; trusts 68 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 57 citations into it; #4,554 of 21,575 by trust here, #8,502 by citations
• Computational Mechanics — 11 citations into it
• Computer Graphics and Computer-Aided Design — 7 citations into it

Whom Mikaël Rousson trusts overall

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

1. Michael E. Leventon
2. Nikos Paragios
3. Stanley J. Osher
4. Jitendra Malik
5. W. Grimson
6. Olivier D. Faugeras
7. Gilles Aubert
8. Rachid Deriche
9. Tony F. Chan
10. Julian Besag

Who trusts Mikaël Rousson overall

1. Daniel Cremers
2. Olga Veksler
3. Nikos Paragios
4. Fuyong Xing
5. Haijun Wang
6. Tinghuai Wang
7. Thomas Brox
8. Kalin S. Kolev
9. Lei He
10. Lin F. Yang

Papers from Mikaël Rousson's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. ImageNet Large Scale Visual Recognition Challenge
9. Attention is All you Need (Neural Information Processing Systems)
10. Going deeper with convolutions

Their papers

• A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape (2007) — 1,158 citations
• Shape Priors for Level Set Representations (2002) — 607 citations
• Active unsupervised texture segmentation on a diffusion based feature space (2003) — 299 citations
• Predicting breast tumor proliferation from whole‐slide images: The TUPAC16 challenge (2018) — 307 citations
• Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets (2009) — 1,124 citations