World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
74
Citations
21118
World Ranking
1501
National Ranking
783

David Doermann publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where David Doermann sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 361 publications — 83rd percentile

83% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

David Doermann D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where David Doermann sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 74 D-Index — 90th percentile

90% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Research.com Recognitions

  • 2016 - IAPR P. Zamperoni Award Content Selection Using Frontalness of Multiple Frames
  • 2014 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to the field of document image analysis and in recognition of service to the IAPR
  • 2014 - IEEE Fellow For contributions to research and development of automatic analysis and processing of document page imaging

Overview

David Doermann is affiliated with the University at Buffalo, State University of New York in the United States. Their research predominantly falls within the field of Computer Science, with a strong focus on Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Aerospace Engineering, and Information Systems.

Their work covers a variety of topics related to advanced computational methods, including:

  • Advanced Neural Network Applications
  • Domain Adaptation and Few-Shot Learning
  • Human Pose and Action Recognition
  • Video Surveillance and Tracking Methods
  • Anomaly Detection Techniques and Applications
  • Advanced Image and Video Retrieval Techniques
  • Adversarial Robustness in Machine Learning

David Doermann has contributed extensively to scholarly publication venues, with multiple papers appearing in:

  • arXiv (Cornell University)
  • International Journal of Computer Vision
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence

Frequent collaborators within their research include Baochang Zhang, Junsong Yuan, Ziyan Wu, Guodong Guo, and Xuan Gong.

Notable recent publications by David Doermann include:

  • YOLOv12: Attention-Centric Real-Time Object Detectors, 2025, arXiv (Cornell University)
  • Ensemble Attention Distillation for Privacy-Preserving Federated Learning, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Chart Mining: A Survey of Methods for Automated Chart Analysis, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Future of software development with generative AI, 2024, Automated Software Engineering
  • Multi-UAV Mobile Edge Computing and Path Planning Platform Based on Reinforcement Learning, 2021, IEEE Transactions on Emerging Topics in Computational Intelligence

In addition to articles and conference papers, David Doermann has published books with Springer International Publishing. One book is titled Neural Networks with Model Compression (2024).

Throughout their career, they have received several awards, including:

  • IAPR P. Zamperoni Award (2016) for work on content selection using frontalness of multiple frames
  • Fellow of the International Association for Pattern Recognition (IAPR) (2014) for contributions to document image analysis and service to IAPR
  • IEEE Fellow (2014) for contributions to research and development of automatic analysis and processing of document page imaging

Best Publications

  • Convolutional Neural Networks for No-Reference Image Quality Assessment

    Le Kang;Peng Ye;Yi Li;David Doermann

  • Automatic text detection and tracking in digital video

    Huiping Li;D. Doermann;O. Kia

  • Text Detection and Recognition in Imagery: A Survey

    Qixiang Ye;David Doermann

  • Unsupervised feature learning framework for no-reference image quality assessment

    Peng Ye;Jayant Kumar;Le Kang;David Doermann

  • Camera-based analysis of text and documents: a survey

    Jian Liang;David Doermann;Huiping Li

  • Towards Optimal Structured CNN Pruning via Generative Adversarial Learning

    Shaohui Lin;Rongrong Ji;Chenqian Yan;Baochang Zhang

  • Blind Image Quality Assessment Based on High Order Statistics Aggregation

    Jingtao Xu;Peng Ye;Qiaohong Li;Haiqing Du

  • The Indexing and Retrieval of Document Images

    David Doermann

  • Video summarization by curve simplification

    Daniel DeMenthon;Vikrant Kobla;David Doermann

  • Progress in camera-based document image analysis

    D. Doermann;Jian Liang;Huiping Li

  • Robust point matching for nonrigid shapes by preserving local neighborhood structures

    Yefeng Zheng;D. Doermann

  • Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networks

    Xiaolong Jiang;Zehao Xiao;Baochang Zhang;Xiantong Zhen

  • No-Reference Image Quality Assessment Using Visual Codebooks

    Peng Ye;D. Doermann

  • Review of Classifier Combination Methods

    Sergey Tulyakov;Stefan Jaeger;Venu Govindaraju;David S. Doermann

  • Script-Independent Text Line Segmentation in Freestyle Handwritten Documents

    Yi Li;Yefeng Zheng;D. Doermann;S. Jaeger

  • Tools and techniques for video performance evaluation

    D. Doermann;D. Mihalcik

  • Machine printed text and handwriting identification in noisy document images

    Yefeng Zheng;Huiping Li;D. Doermann

  • Hierarchical Part-Template Matching for Human Detection and Segmentation

    Zhe Lin;L.S. Davis;D. Doermann;D. DeMenthon

  • Geometric Rectification of Camera-Captured Document Images

    Jian Liang;D. DeMenthon;D. Doermann

  • Handbook of Document Image Processing and Recognition

    David Doermann;Karl Tombre

Frequent Co-Authors

Daniel DeMenthon
Daniel DeMenthon Johns Hopkins University Applied Physics Laboratory
Baochang Zhang
Baochang Zhang Beihang University
Azriel Rosenfeld
Azriel Rosenfeld University of Maryland, College Park
Yefeng Zheng
Yefeng Zheng Tencent (China)
Larry S. Davis
Larry S. Davis University of Maryland, College Park
Douglas W. Oard
Douglas W. Oard University of Maryland, College Park
Rongrong Ji
Rongrong Ji Xiamen University
Jianzhuang Liu
Jianzhuang Liu Shenzhen Institutes of Advanced Technology
Qixiang Ye
Qixiang Ye Chinese Academy of Sciences
Ehud Rivlin
Ehud Rivlin Technion – Israel Institute of Technology

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