F.K. Lam

1.6k citations
76 papers · 1.1k · h-index 17

Impact in

Papers in

F.K. Lam

66 papers receiving 1.0k citations

Peers

F.K. Lam
Comparison fields: 5 of 121
  • Cognitive Neuroscience 373
  • Signal Processing 184
  • Computer Vision and Pattern Recognition 349
  • Human-Computer Interaction 86
  • Cellular and Molecular Neuroscience 165
Replace Kyoobin Lee with:
Kyoobin Lee South Korea
Noboru Sugie Japan
Zhu Liang Yu China
Bin Yan China
J. E. W. Mayhew United Kingdom
George K. Hung United States
Sugata Munshi India
Jianting Cao Japan
Humaira Nisar Malaysia
Seong‐Eun Kim South Korea
F.K. Lam relative to Kyoobin Lee South Korea Kyoobin Lee's profile →
Citations per field
00.5×2.6×
Kyoobin Lee · 1×
Citations per year

Countries citing papers authored by F.K. Lam

Since Specialization
Citations

This map shows the geographic impact of F.K. Lam's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by F.K. Lam with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites F.K. Lam more than expected).

Fields of papers citing papers by F.K. Lam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by F.K. Lam. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by F.K. Lam. The network helps show where F.K. Lam may publish in the future.

Co-authors

The 25 scholars most cited alongside F.K. Lam, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with F.K. Lam Line = papers co-authored together F.K. Lam links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 76 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2000328
2 1999119
3 199647
4 199740
5 200238
6 199534
7 199433
8 199732
9 199227
10 199725
11 199824
12 198223
13 199623
14 200621
15 199917
16 199617
17 200217
18 200215
19 199215
20 199714

About F.K. Lam

F.K. Lam is a scholar working on Computer Vision and Pattern Recognition, Signal Processing, Cognitive Neuroscience, Mechanics of Materials and Artificial Intelligence, having authored 76 papers that have together received 1.1k indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (14 papers), Blind Source Separation Techniques (14 papers), Image and Signal Denoising Methods (10 papers), Image and Object Detection Techniques (9 papers), Neural Networks and Applications (9 papers), Neural dynamics and brain function (9 papers), Advanced Adaptive Filtering Techniques (8 papers) and Flow Measurement and Analysis (8 papers). The work is most often cited by research in Cognitive Neuroscience (373 citations), Signal Processing (184 citations), Computer Vision and Pattern Recognition (349 citations), Human-Computer Interaction (86 citations) and Cellular and Molecular Neuroscience (165 citations). F.K. Lam has collaborated with scholars based in Hong Kong, Taiwan and China. Frequent co-authors include F.H.Y. Chan, F.H.Y. Chan, Yuan‐Ting Zhang, P.A. Parker, Yongsheng Yang, P.W.F. Poon, Hui Zhu, P.W.M. Tsang, Pong C. Yuen and Weihong Qiu. Their work appears in journals such as IEEE Transactions on Biomedical Engineering, Ultrasonics, Pattern Recognition, Bio-Medical Materials and Engineering and Pattern Recognition Letters.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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