B. Roysam
Computer Vision and Pattern Recognition · 2002–2012
A researcher in the Science corpus: 12 papers; trusts 60 researchers and is trusted by 27 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 78 citations into it; #5,393 of 21,575 by trust here, #5,116 by citations
- Biophysics — 39 citations into it; #290 of 1,196 by trust here, #275 by citations
- Molecular Biology — 38 citations into it; #6,999 of 17,773 by trust here, #5,041 by citations
- Artificial Intelligence — 14 citations into it; #22,403 of 35,345 by trust here, #19,439 by citations
- Developmental Neuroscience — 14 citations into it
- Media Technology — 13 citations into it; #1,832 of 3,621 by trust here, #1,135 by citations
- Medical Imaging — 11 citations into it
- Aerospace Engineering — 7 citations into it; #1,468 of 6,421 by trust here, #3,072 by citations
Whom B. Roysam trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Stéphane Mallat
- Gunilla Borgefors
- Stanley J. Osher
- Yuri Boykov
- David G. Lowe
- David L. Donoho
- Philip H. S. Torr
- Gang Lin
- Luc M. Vincent
- Peter J. Rousseeuw
Who trusts B. Roysam overall
- Fuyong Xing
- Aristeidis Sotiras
- Lin F. Yang
- Erik H. W. Meijering
- Francisco P. M. Oliveira
- Vaidy Mani
- Charles V. Stewart
- Francesca Bovolo
Papers from B. Roysam's perspective
Ordered by how strongly the researchers B. Roysam trusts endorse each paper. Same corpus, a different order for every researcher.
- Distinctive Image Features from Scale-Invariant Keypoints
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Normalized cuts and image segmentation
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Their papers
- Biological Imaging Software Tools (2012) — 513 citations
- Adult SVZ stem cells lie in a vascular niche: A quantitative analysis of niche cell-cell interactions (2008) — 1,065 citations
- Hierarchical, model‐based merging of multiple fragments for improved three‐dimensional segmentation of nuclei (2005) — 121 citations
- A hyperfused mitochondrial state achieved at G1–S regulates cyclin E buildup and entry into S phase (2009) — 628 citations
- Coupled minimum-cost flow cell tracking for high-throughput quantitative analysis (2011) — 162 citations
- Automated Cell Lineage Construction: A Rapid Method to Analyze Clonal Development Established with Murine Neural Progenitor Cells (2006) — 191 citations
- Improved Automatic Detection and Segmentation of Cell Nuclei in Histopathology Images (2010) — 716 citations
- Mapping behaviorally relevant neural circuits with immediate-early gene expression. (2005) — 411 citations
- Image change detection algorithms: a systematic survey (2005) — 2,150 citations
- The dual-bootstrap iterative closest point algorithm with application to retinal image registration (2003) — 410 citations
- A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks (2003) — 343 citations
- A Feature-Based, Robust, Hierarchical Algorithm for Registering Pairs of Images of the Curved Human Retina (2002) — 324 citations