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Minh N. Do

D-Index & Metrics

Discipline name D-Index World Ranking Current World Ranking National Ranking Current National Ranking Publications Citations
Computer Science 71 1737 1683 885 852 317 29804

Minh N. Do publications per year

The chart shows the history of publications by Minh N. Do between 1998 and 2021, highlighting the no. of papers published in each year and offering an overview of the publication velocity of this scholar. Minh N. Do published across 24 years, from 1998 to 2021, averaging 13.3 papers a year. Output peaked at 27 publications in 2018. 24 of the 318 publications appeared in the last two years.

No. of publications
5 10 15 20 25
Bar chart. Horizontal axis: year, 1998 to 2021. Vertical axis: number of publications, 0 to 27. Peak 27 publications in 2018. 1998: 1 publication 1999: 1 publication 2000: 5 publications 2001: 5 publications 2002: 11 publications 2003: 13 publications 2004: 6 publications 2005: 20 publications 2006: 16 publications 2007: 17 publications 2008: 10 publications 2009: 17 publications 2010: 6 publications 2011: 15 publications 2012: 20 publications 2013: 17 publications 2014: 14 publications 2015: 17 publications 2016: 19 publications 2017: 22 publications 2018: 27 publications 2019: 15 publications 2020: 12 publications 2021: 12 publications
1998 2021

318 publications in total across all disciplines

View publications per year as a table
Minh N. Do: publications per year, 1998 to 2021
Year Publications
1998 1
1999 1
2000 5
2001 5
2002 11
2003 13
2004 6
2005 20
2006 16
2007 17
2008 10
2009 17
2010 6
2011 15
2012 20
2013 17
2014 14
2015 17
2016 19
2017 22
2018 27
2019 15
2020 12
2021 12
Total 318
Download as CSV

Minh N. Do 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 Minh N. Do sits on this spectrum.

No. of scientists
200 400 600
Bar chart with 97 bars. Horizontal axis: publications, 32–41 to 991+. Vertical axis: number of scientists, 0 to 609. Most scientists, 609, have 142–151 publications. The last bar groups every scientist with 991 publications or more. The highlighted bar, 312–321 publications, is where this scientist sits. 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
32–41 publications 991+

This scientist: 317 publications — 77th percentile

77% of scientists in this discipline score the same or lower.

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

View publications distribution as a table
Number of Computer Science scientists by publication count, Research.com 2026 ranking edition. Based on 14,188 ranked scientists.
Publications Scientists This scientist
32–41 7
42–51 22
52–61 82
62–71 134
72–81 249
82–91 324
92–101 421
102–111 420
112–121 497
122–131 544
132–141 555
142–151 609
152–161 559
162–171 534
172–181 556
182–191 583
192–201 519
202–211 508
212–221 490
222–231 437
232–241 423
242–251 408
252–261 377
262–271 301
272–281 335
282–291 320
292–301 293
302–311 250
312–321 238 317
322–331 206
332–341 209
342–351 208
352–361 162
362–371 176
372–381 127
382–391 158
392–401 128
402–411 104
412–421 94
422–431 99
432–441 83
442–451 108
452–461 73
462–471 77
472–481 69
482–491 84
492–501 62
502–511 54
512–521 57
522–531 51
532–541 51
542–551 32
552–561 38
562–571 28
572–581 43
582–591 33
592–601 41
602–611 32
612–621 28
622–631 25
632–641 27
642–651 17
652–661 20
662–671 17
672–681 15
682–691 14
692–701 21
702–711 13
712–721 12
722–731 19
732–741 14
742–751 12
752–761 10
762–771 10
772–781 11
782–791 10
792–801 11
802–811 8
812–821 8
822–831 7
832–841 11
842–851 10
852–861 5
862–871 9
872–881 4
882–891 6
892–901 3
902–911 6
912–921 3
922–931 2
932–941 2
942–951 2
952–961 3
962–971 3
972–981 3
982–990 5
991+ 100
Download as CSV

Minh N. Do 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 Minh N. Do sits on this spectrum.

No. of scientists
200 400 600 800
Bar chart with 52 bars. Horizontal axis: D-Index, 30–31 to 131+. Vertical axis: number of scientists, 0 to 990. Most scientists, 990, have 36–37 D-Index. The last bar groups every scientist with 131 D-Index or more. The highlighted bar, 70–71 D-Index, is where this scientist sits. 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
30–31 D-Index 131+

This scientist: 71 D-Index — 88th percentile

88% of scientists in this discipline score the same or lower.

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

View D-Index distribution as a table
Number of Computer Science scientists by D-index, Research.com 2026 ranking edition. Based on 14,188 ranked scientists.
D-Index Scientists This scientist
30–31 879
32–33 983
34–35 918
36–37 990
38–39 968
40–41 907
42–43 821
44–45 763
46–47 689
48–49 543
50–51 543
52–53 518
54–55 500
56–57 458
58–59 400
60–61 337
62–63 308
64–65 292
66–67 249
68–69 213
70–71 192 71
72–73 189
74–75 165
76–77 139
78–79 119
80–81 121
82–83 113
84–85 88
86–87 87
88–89 75
90–91 69
92–93 57
94–95 46
96–97 38
98–99 34
100–101 36
102–103 27
104–105 37
106–107 18
108–109 31
110–111 19
112–113 16
114–115 12
116–117 20
118–119 15
120–121 5
122–123 20
124–125 8
126–127 5
128–129 7
130 3
131+ 98
Download as CSV

Research.com Recognitions

  • 2014 - IEEE Fellow For contributions to image representation and computational imaging

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Algorithm

His primary areas of investigation include Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Wavelet transform. His Computer vision study combines topics from a wide range of disciplines, such as Salient and Process. He has included themes like Kullback–Leibler divergence, Inpainting, Image, Inference and Generative model in his Pattern recognition study.

His work on Computational complexity theory is typically connected to Cartesian tensor as part of general Algorithm study, connecting several disciplines of science. His Wavelet transform study deals with the bigger picture of Wavelet. His Contourlet research incorporates elements of Filter bank and Curvelet.

His most cited work include:

  • The contourlet transform: an efficient directional multiresolution image representation (3282 citations)
  • The Nonsubsampled Contourlet Transform: Theory, Design, and Applications (1545 citations)
  • Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance (1065 citations)

What are the main themes of his work throughout his whole career to date?

Minh N. Do mostly deals with Artificial intelligence, Computer vision, Algorithm, Pattern recognition and Wavelet. His research in Image processing, Pixel, Wavelet transform, Image and Contourlet are components of Artificial intelligence. Many of his studies involve connections with topics such as Filter and Contourlet.

His Computer vision study often links to related topics such as Computer graphics. His Algorithm research focuses on Filter bank and how it relates to Filter design. His work is dedicated to discovering how Wavelet, Image retrieval are connected with Image texture and other disciplines.

He most often published in these fields:

  • Artificial intelligence (59.02%)
  • Computer vision (40.06%)
  • Algorithm (24.77%)

What were the highlights of his more recent work (between 2016-2021)?

  • Artificial intelligence (59.02%)
  • Computer vision (40.06%)
  • Pattern recognition (16.82%)

In recent papers he was focusing on the following fields of study:

Minh N. Do focuses on Artificial intelligence, Computer vision, Pattern recognition, Feature and Machine learning. All of his Artificial intelligence and Deep learning, Object detection, Optical flow, Convolution and Minimum bounding box investigations are sub-components of the entire Artificial intelligence study. His Convolution study also includes

  • Component, which have a strong connection to Constraint, Focus, Feature vector and Pixel,
  • Artificial neural network that intertwine with fields like Elastic net regularization, Radiology and Modality.

His work on RGB color model, Object and Image based as part of general Computer vision study is frequently linked to CAD and Track, therefore connecting diverse disciplines of science. Convolutional neural network is the focus of his Pattern recognition research. His work in Feature tackles topics such as Image stitching which are related to areas like Trajectory, Homography and Position.

Between 2016 and 2021, his most popular works were:

  • Semantic Image Inpainting with Deep Generative Models (665 citations)
  • Efficient Tensor Completion for Color Image and Video Recovery: Low-Rank Tensor Train (134 citations)
  • Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning. (54 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Computer vision
  • Algorithm

Minh N. Do mainly focuses on Artificial intelligence, Computer vision, Deep learning, Machine learning and Algorithm. His research on Artificial intelligence frequently links to adjacent areas such as Pattern recognition. The various areas that he examines in his Pattern recognition study include Similarity and Pattern matching.

In general Computer vision, his work in Video processing, Shape analysis and Geometric primitive is often linked to Geometry processing linking many areas of study. In his study, Random forest and Test set is strongly linked to Image segmentation, which falls under the umbrella field of Machine learning. His research investigates the connection between Algorithm and topics such as Tensor that intersect with issues in Dimension, Computation, Compression and Matrix decomposition.

Best Publications

  • The contourlet transform: an efficient directional multiresolution image representation

    M.N. Do;M. Vetterli

  • The Nonsubsampled Contourlet Transform: Theory, Design, and Applications

    A.L. da Cunha;Jianping Zhou;M.N. Do

  • Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance

    M.N. Do;M. Vetterli

  • Semantic Image Inpainting with Deep Generative Models

    Raymond A. Yeh;Chen Chen;Teck Yian Lim;Alexander G. Schwing;Alexander G. Schwing

  • The finite ridgelet transform for image representation

    M.N. Do;M. Vetterli

  • Directional multiscale modeling of images using the contourlet transform

    D.D.-Y. Po;M.N. Do

  • Contourlets: a directional multiresolution image representation

    M.N. Do;M. Vetterli

  • A Multi-Organ Nucleus Segmentation Challenge

    Neeraj Kumar;Ruchika Verma;Deepak Anand;Yanning Zhou

  • Semantic Image Inpainting with Perceptual and Contextual Losses.

    Raymond A. Yeh;Chen Chen;Teck-Yian Lim;Mark Hasegawa-Johnson

  • Efficient Tensor Completion for Color Image and Video Recovery: Low-Rank Tensor Train

    Johann A. Bengua;Ho N. Phien;Hoang Duong Tuan;Minh N. Do

  • Framing pyramids

    M.N. Do;M. Vetterli

  • Directional multiresolution image representations

    Minh N. Do

  • Fast global image smoothing based on weighted least squares.

    Dongbo Min;Sunghwan Choi;Jiangbo Lu;Bumsub Ham

  • A Theory for Sampling Signals From a Union of Subspaces

    Y.M. Lu;M.N. Do

  • Rotation invariant texture characterization and retrieval using steerable wavelet-domain hidden Markov models

    M.N. Do;M. Vetterli

  • Depth Video Enhancement Based on Weighted Mode Filtering

    Dongbo Min;Jiangbo Lu;M. N. Do

  • Fast approximation of Kullback-Leibler distance for dependence trees and hidden Markov models

    M.N. Do

  • Multidimensional Directional Filter Banks and Surfacelets

    Y.M. Lu;M.N. Do

  • Nonsubsampled contourlet transform: construction and application in enhancement

    Jianping Zhou;A.L. Cunha;M.N. Do

  • Pyramidal directional filter banks and curvelets

    M.N. Do;M. Vetterli

Frequent Co-Authors

Martin Vetterli
Martin Vetterli École Polytechnique Fédérale de Lausanne
Jiangbo Lu
Jiangbo Lu SmartMore Corporation
Dongbo Min
Dongbo Min Ewha Womans University
Yue M. Lu
Yue M. Lu Beijing University of Posts and Telecommunications
Sanjay J. Patel
Sanjay J. Patel University of Illinois at Urbana-Champaign
Michael L. Oelze
Michael L. Oelze University of Illinois at Urbana-Champaign
Anan Liu
Anan Liu Tianjin University
Weizhi Nie
Weizhi Nie Tianjin University
Charles A. Bouman
Charles A. Bouman Purdue University West Lafayette
Hoang Duong Tuan
Hoang Duong Tuan University of Technology Sydney

External Links

Wikipedia of Minh N. Do Personal website of Minh N. Do Google Scholar page

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