K. Plataniotis

Computer Vision and Pattern Recognition · 2000–2021

A researcher in the Science corpus: 15 papers; trusts 233 researchers and is trusted by 46 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 99 citations into it; #4,456 of 21,575 by trust here, #5,711 by citations
• Signal Processing — 46 citations into it; #1,338 of 7,572 by trust here, #2,184 by citations
• Artificial Intelligence — 44 citations into it; #16,690 of 35,345 by trust here, #17,412 by citations
• Electrical and Electronic Engineering — 21 citations into it; #7,742 of 16,539 by trust here, #13,553 by citations
• Computational Mathematics — 17 citations into it; #375 of 612 by trust here, #372 by citations
• Biomedical Engineering — 16 citations into it; #2,143 of 10,734 by trust here, #6,773 by citations
• Information Systems — 16 citations into it
• Cognitive Neuroscience — 14 citations into it; #9,351 of 10,852 by trust here, #9,744 by citations

Whom K. Plataniotis trusts overall

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

1. Matthew A. Turk
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Jieping Ye
6. M. Alex O. Vasilescu
7. P. Jonathon Phillips
8. Peter N. Belhumeur
9. Ilya Sutskever
10. Yann LeCun

Who trusts K. Plataniotis overall

1. Brendan Klare
2. Haiping Lu
3. Samir S. Yadav
4. Alaa Tharwat
5. Jiwen Lu
6. Anil K. Jain
7. Wadhah Ayadi
8. Milica M. Badža
9. Andrzej S. Cichocki
10. Qun Li

Papers from K. Plataniotis's perspective

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

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

Their papers

• MPCA: Multilinear Principal Component Analysis of Tensor Objects (2008) — 907 citations
• Kernel-Based Positioning in Wireless Local Area Networks (2007) — 443 citations
• Gait recognition: a challenging signal processing technology for biometric identification (2005) — 417 citations
• Vector filtering for color imaging (2005) — 317 citations
• Deep learning and machine vision for food processing: A survey (2021) — 319 citations
• COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images (2020) — 629 citations
• Capsule Networks for Brain Tumor Classification Based on MRI Images and Coarse Tumor Boundaries (2018) — 338 citations
• Brain Tumor Type Classification via Capsule Networks (2018) — 510 citations
• A survey of multilinear subspace learning for tensor data (2011) — 380 citations
• Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting (2010) — 316 citations
• An Analysis of Random Projection for Changeable and Privacy-Preserving Biometric Verification (2010) — 69 citations
• Analysis of Human Electrocardiogram for Biometric Recognition (2008) — 399 citations
• Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition (2005) — 332 citations
• Face recognition using kernel direct discriminant analysis algorithms (2003) — 698 citations
• Color Image Processing and Applications (2000) — 683 citations