World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
50
Citations
9507
World Ranking
5640
National Ranking
173

Brian C. Lovell 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 Brian C. Lovell 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: 328 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.

Brian C. Lovell 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 Brian C. Lovell 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: 50 D-Index — 62nd percentile

62% 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

  • 2008 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to video and medical image analysis and service to the IAPR.

Overview

Brian C. Lovell is affiliated with the University of Queensland in Australia. Their research work spans the fields of Computer Science and Physics and Astronomy, with a significant focus on subfields such as Astronomy and Astrophysics, Computer Vision and Pattern Recognition, Artificial Intelligence, Geophysics, and Radiology, Nuclear Medicine and Imaging.

Their research covers several main topics, including:

  • Ionosphere and magnetosphere dynamics
  • Solar and Space Plasma Dynamics
  • Domain Adaptation and Few-Shot Learning
  • Earthquake Detection and Analysis
  • Advanced Neural Network Applications
  • Astro and Planetary Science
  • Multimodal Machine Learning Applications

Brian C. Lovell has published extensively, with frequent contributions to the following venues:

  • Journal of Geophysical Research Space Physics
  • arXiv (Cornell University)
  • Atmosphere
  • Computer Vision and Image Understanding
  • Preprints.org

Among their recent papers are:

  • Faster ILOD: Incremental learning for object detectors based on faster RCNN, 2020, Pattern Recognition Letters
  • SID: Incremental learning for anchor-free object detection via Selective and Inter-related Distillation, 2021, Computer Vision and Image Understanding
  • Investigating the Coupled Magnetosphere-Ionosphere-Thermosphere (M-I-T) System's Responses to the 20 November 2003 Superstorm, 2021, Journal of Geophysical Research Space Physics
  • EBIT: Weakly-supervised image translation with edge and boundary enhancement, 2020, Pattern Recognition Letters
  • Minimizing Labeling Cost for Nuclei Instance Segmentation and Classification with Cross-domain Images and Weak Labels, 2021, Proceedings of the AAAI Conference on Artificial Intelligence

They have frequently collaborated with several co-authors, including:

  • Ildiko Horvath
  • Can Peng
  • Kun Zhao
  • Meng Li
  • Sam Maksoud

In 2008, Brian C. Lovell was awarded the status of Fellow of the International Association for Pattern Recognition (IAPR) for contributions to video and medical image analysis and service to the IAPR.

Best Publications

  • Unsupervised Domain Adaptation by Domain Invariant Projection

    Mahsa Baktashmotlagh;Mahsa Baktashmotlagh;Mehrtash T. Harandi;Mehrtash T. Harandi;Brian C. Lovell;Mathieu Salzmann;Mathieu Salzmann

  • Shadow detection: A survey and comparative evaluation of recent methods

    Andres Sanin;Conrad Sanderson;Brian C. Lovell

  • Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition

    Yongkang Wong;Shaokang Chen;Sandra Mau;Conrad Sanderson

  • Multi-Region Probabilistic Histograms for Robust and Scalable Identity Inference

    Conrad Sanderson;Brian C. Lovell

  • Graph embedding discriminant analysis on Grassmannian manifolds for improved image set matching

    Mehrtash T. Harandi;Conrad Sanderson;Sareh Shirazi;Brian C. Lovell

  • Unsupervised cell nucleus segmentation with active contours

    Pascal Bamford;Brian Lovell

  • Improved anomaly detection in crowded scenes via cell-based analysis of foreground speed, size and texture

    Vikas Reddy;Conrad Sanderson;Brian C. Lovell

  • Sparse coding and dictionary learning for symmetric positive definite matrices: a kernel approach

    Mehrtash T. Harandi;Conrad Sanderson;Richard Hartley;Brian C. Lovell

  • Spatio-temporal covariance descriptors for action and gesture recognition

    A. Sanin;C. Sanderson;M. T. Harandi;B. C. Lovell

  • Improved Shadow Removal for Robust Person Tracking in Surveillance Scenarios

    Andres Sanin;Conrad Sanderson;Brian C. Lovell

  • The statistical performance of some instantaneous frequency estimators

    B.C. Lovell;R.C. Williamson

  • Dictionary Learning and Sparse Coding on Grassmann Manifolds: An Extrinsic Solution

    Mehrtash Harandi;Conrad Sanderson;Chunhua Shen;Brian Lovell

  • Face Recognition on Consumer Devices: Reflections on Replay Attacks

    Daniel F. Smith;Arnold Wiliem;Brian C. Lovell

  • Domain Adaptation on the Statistical Manifold

    Mahsa Baktashmotlagh;Mehrtash T. Harandi;Brian C. Lovell;Mathieu Salzmann

  • Faster ILOD: Incremental learning for object detectors based on faster RCNN

    Can Peng;Kun Zhao;Brian C. Lovell

  • Kernel analysis over Riemannian manifolds for visual recognition of actions, pedestrians and textures

    Mehrtash T. Harandi;Conrad Sanderson;Arnold Wiliem;Brian C. Lovell

  • Improved Foreground Detection via Block-Based Classifier Cascade With Probabilistic Decision Integration

    V. Reddy;C. Sanderson;B. C. Lovell

  • TV-GAN: Generative Adversarial Network Based Thermal to Visible Face Recognition

    Teng Zhang;Arnold Wiliem;Siqi Yang;Brian Lovell

  • A low-complexity algorithm for static background estimation from cluttered image sequences in surveillance contexts

    Vikas Reddy;Conrad Sanderson;Brian C. Lovell

  • Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds

    Mehrtash Harandi;Richard Hartley;Chunhua Shen;Brian Lovell

  • Corner detection based on gradient correlation matrices of planar curves

    Xiaohong Zhang;Hongxing Wang;Andrew W. B. Smith;Xu Ling

  • Digital Image Computing: Techniques and Applications (DICTA 2005)

    B. C. Lovell;A. J. Maeder;T. Caelli;S. Ourselin

Frequent Co-Authors

Conrad Sanderson
Conrad Sanderson Commonwealth Scientific and Industrial Research Organisation
Mehrtash Harandi
Mehrtash Harandi Monash University
Richard A. Davis
Richard A. Davis Columbia University
Boualem Boashash
Boualem Boashash University of Queensland
Andrew P. Bradley
Andrew P. Bradley Queensland University of Technology
Mario Vento
Mario Vento University of Salerno
Mathieu Salzmann
Mathieu Salzmann École Polytechnique Fédérale de Lausanne
Richard Hartley
Richard Hartley Australian National University
Chunhua Shen
Chunhua Shen Zhejiang University
Rodney F. Minchin
Rodney F. Minchin University of Queensland

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