Daniel F. Huber

Computer Vision and Pattern Recognition · 2003–2011

A researcher in the Science corpus: 7 papers; trusts 32 researchers and is trusted by 40 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 54 citations into it; #6,795 of 21,575 by trust here, #6,737 by citations
• Aerospace Engineering — 22 citations into it; #468 of 6,421 by trust here, #624 by citations
• Geology — 21 citations into it; #678 of 687 by trust here, #371 by citations
• Computational Mechanics — 15 citations into it; #2,208 of 10,001 by trust here, #3,275 by citations
• Computer Graphics and Computer-Aided Design — 13 citations into it; #367 of 1,584 by trust here, #827 by citations
• Environmental Engineering — 9 citations into it; #4,689 of 5,479 by trust here, #1,912 by citations

Whom Daniel F. Huber trusts overall

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

1. Brian Curless
2. Marc Levoy
3. Richard J. Campbell
4. Michael Garland
5. Martial Hebert
6. Szymon Rusinkiewicz
7. Andrew E. Johnson
8. Patrick J. Flynn
9. Paul S. Heckbert
10. Shigeo Abe

Who trusts Daniel F. Huber overall

1. Qian-Yi Zhou
2. M. Bosse
3. Burcu Akinci
4. Federico Tombari
5. Xuehan Xiong
6. Yulan Guo
7. Sungjoon Choi
8. Samuele Salti
9. Jiaolong Yang
10. Robert R. Lipman

Papers from Daniel F. Huber's perspective

Ordered by how strongly the researchers Daniel F. Huber 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Distinctive Image Features from Scale-Invariant Keypoints
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Recognizing Objects in Range Data Using Regional Point Descriptors (2004) — 985 citations
• Rapid object indexing using locality sensitive hashing and joint 3D-signature space estimation (2006) — 100 citations
• Automatic Creation of Semantically Rich 3D Building Models from Laser Scanner Data (2011) — 744 citations
• Automatic Reconstruction of As-Built Building Information Models from Laser-Scanned Point Clouds: A Review of Related Techniques | NIST (2010) — 1,114 citations
• Natural terrain classification using three‐dimensional ladar data for ground robot mobility (2006) — 512 citations
• A formalism for utilization of sensor systems and integrated project models for active construction quality control (2006) — 352 citations
• Fully automatic registration of multiple 3D data sets (2003) — 421 citations