World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
43
Citations
14960
World Ranking
7761
National Ranking
314

Paul Fieguth 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 Paul Fieguth 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: 323 publications — 78th percentile

78% of scientists in this discipline score the same or lower.

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

Paul Fieguth 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 Paul Fieguth 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: 43 D-Index — 46th percentile

46% of scientists in this discipline score the same or lower.

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

Overview

Paul Fieguth is affiliated with the University of Waterloo in Canada and has a significant research presence in the fields of computer science and engineering. Their work spans multiple subfields including computer vision and pattern recognition, biomedical engineering, artificial intelligence, mechanics of materials, and mechanical engineering.

The scientist has focused research efforts on topics such as photoacoustic and ultrasonic imaging, thermography and photoacoustic techniques, optical measurement and interference techniques, advanced image and video retrieval techniques, optical coherence tomography applications, advanced neural network applications, and advanced vision and imaging.

Frequent publication venues for their work include arXiv (Cornell University), with 24 publications, followed by the Journal of Computational Vision and Imaging Systems with 6 publications, Scientific Reports with 5, Zenodo (CERN European Organization for Nuclear Research) with 4, and IEEE Access with 4.

Paul Fieguth has collaborated extensively with colleagues such as Parsin Haji Reza, Nicholas Pellegrino, Benjamin R. Ecclestone, Mohamed A. Naiel, and Amir Nazemi, with joint publications numbering 18, 18, 12, 11, and 10 respectively.

Recent papers authored or co-authored by Paul Fieguth include:

  • A review of uncertainty quantification in deep learning: Techniques, applications and challenges, 2021, Information Fusion
  • Deep Learning for Instance Retrieval: A Survey, 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • A new concordant partial AUC and partial c statistic for imbalanced data in the evaluation of machine learning algorithms, 2020, BMC Medical Informatics and Decision Making
  • Virtual histological staining of label-free total absorption photoacoustic remote sensing (TA-PARS), 2022, Scientific Reports
  • Process performance evaluation and classification via in-situ melt pool monitoring in directed energy deposition, 2021, CIRP Journal of Manufacturing Science and Technology

Best Publications

  • Deep Learning for Generic Object Detection: A Survey

    Li Liu;Li Liu;Wanli Ouyang;Xiaogang Wang;Paul W. Fieguth

  • A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges

    Moloud Abdar;Farhad Pourpanah;Sadiq Hussain;Dana Rezazadegan

  • A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

    Christian Koch;Kristina Georgieva;Varun Kasireddy;Burcu Akinci

  • Median Robust Extended Local Binary Pattern for Texture Classification

    Li Liu;Songyang Lao;Paul W. Fieguth;Yulan Guo

  • Color-based tracking of heads and other mobile objects at video frame rates

    P. Fieguth;D. Terzopoulos

  • Local binary features for texture classification

    Li Liu;Paul Fieguth;Yulan Guo;Xiaogang Wang

  • Extended local binary patterns for texture classification

    Li Liu;Lingjun Zhao;Yunli Long;Gangyao Kuang

  • Texture Classification from Random Features

    Li Liu;Paul Fieguth

  • From BoW to CNN: Two Decades of Texture Representation for Texture Classification

    Li Liu;Li Liu;Jie Chen;Paul W. Fieguth;Guoying Zhao

  • Automated detection of cracks in buried concrete pipe images

    Sunil K. Sinha;Paul W. Fieguth

  • Median robust extended local binary pattern for texture classification

    Li Liu;Paul Fieguth;Matti Pietikainen;Songyang Lao

  • BRINT: Binary Rotation Invariant and Noise Tolerant Texture Classification

    Li Liu;Yunli Long;Paul W. Fieguth;Songyang Lao

  • Deep Learning for Instance Retrieval: A Survey

    Unknown

  • Adaptive Wiener filtering of noisy images and image sequences

    F. Jin;P. Fieguth;L. Winger;E. Jernigan

  • Multiresolution optimal interpolation and statistical analysis of TOPEX/POSEIDON satellite altimetry

    Paul W. Fieguth;William C. Karl;Alan S. Willsky;Carl Wunsch

  • Extended local binary patterns for face recognition

    Li Liu;Paul Fieguth;Guoying Zhao;Matti Pietikäinen

  • Automatic Skin Lesion Segmentation via Iterative Stochastic Region Merging

    A. Wong;J. Scharcanski;P. Fieguth

  • Segmentation of buried concrete pipe images

    Sunil K. Sinha;Paul W. Fieguth

  • A new concordant partial AUC and partial c statistic for imbalanced data in the evaluation of machine learning algorithms

    André M. Carrington;Paul W. Fieguth;Hammad Qazi;Andreas Holzinger;Andreas Holzinger

  • Statistical Image Processing and Multidimensional Modeling

    Paul Fieguth

  • Neuro-fuzzy network for the classification of buried pipe defects

    Sunil K. Sinha;Paul W. Fieguth

  • Decoupled Active Contour (DAC) for Boundary Detection

    Akshaya Kumar Mishra;Paul W Fieguth;David A Clausi

Frequent Co-Authors

Alexander Wong
Alexander Wong University of Waterloo
David A. Clausi
David A. Clausi University of Waterloo
Matti Pietikäinen
Matti Pietikäinen University of Oulu
William Clement Karl
William Clement Karl Boston University
Gangyao Kuang
Gangyao Kuang National University of Defense Technology
Guoying Zhao
Guoying Zhao University of Oulu
Xiaogang Wang
Xiaogang Wang Chinese University of Hong Kong
Carl Wunsch
Carl Wunsch Harvard University
Maria Anna Polak
Maria Anna Polak University of Waterloo

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