Dar-Shyang Lee

1.5k total citations · 1 hit paper
22 papers, 1.0k citations indexed

About

Dar-Shyang Lee is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Dar-Shyang Lee has authored 22 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Computer Vision and Pattern Recognition, 8 papers in Artificial Intelligence and 7 papers in Signal Processing. Recurrent topics in Dar-Shyang Lee's work include Handwritten Text Recognition Techniques (9 papers), Video Analysis and Summarization (8 papers) and Music and Audio Processing (6 papers). Dar-Shyang Lee is often cited by papers focused on Handwritten Text Recognition Techniques (9 papers), Video Analysis and Summarization (8 papers) and Music and Audio Processing (6 papers). Dar-Shyang Lee collaborates with scholars based in United States and Japan. Dar-Shyang Lee's co-authors include Sargur N. Srihari, Ray Smith, J.J. Hull, Jonathan J. Hull, B. Erol, Jamey Graham, Yongchul Shin, Sargur Srihari, Alexander N. Gorban and Steven L. Schwarcz and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Proceedings of the IEEE and Pattern Recognition Letters.

In The Last Decade

Dar-Shyang Lee

21 papers receiving 947 citations

Hit Papers

Effective Gaussian mixture learning for video background ... 2005 2026 2012 2019 2005 200 400 600
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Effective Gaussian mixture learning for video background subtraction
    2005 614 citations Dar-Shyang Lee IEEE Transactions on Pattern Analysis and Machine Intelligence profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Dar-Shyang Lee United States 11 856 222 114 89 66 22 1.0k
Benjamin Höferlin Germany 11 774 0.9× 165 0.7× 96 0.8× 73 0.8× 45 0.7× 19 880
Yao Lu China 15 705 0.8× 238 1.1× 93 0.8× 64 0.7× 38 0.6× 71 914
Weina Ge United States 11 947 1.1× 397 1.8× 154 1.4× 42 0.5× 83 1.3× 16 1.1k
M. Lu United States 6 547 0.6× 123 0.6× 35 0.3× 57 0.6× 44 0.7× 6 640
Supavadee Aramvith Thailand 16 578 0.7× 162 0.7× 127 1.1× 209 2.3× 16 0.2× 116 851
Lambert E. Wixson United States 12 1.2k 1.4× 221 1.0× 63 0.6× 49 0.6× 122 1.8× 16 1.3k
Enrique Cabello Spain 13 342 0.4× 131 0.6× 34 0.3× 177 2.0× 47 0.7× 57 609
Rin-ichiro Taniguchi Japan 18 665 0.8× 106 0.5× 117 1.0× 56 0.6× 16 0.2× 99 889
Mike Zheng Shou Singapore 18 1.1k 1.3× 451 2.0× 30 0.3× 119 1.3× 26 0.4× 74 1.4k
Gurjit Singh Walia India 16 591 0.7× 176 0.8× 100 0.9× 275 3.1× 49 0.7× 45 799

Countries citing papers authored by Dar-Shyang Lee

Since Specialization
Citations

This map shows the geographic impact of Dar-Shyang Lee'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 Dar-Shyang Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dar-Shyang Lee more than expected).

Fields of papers citing papers by Dar-Shyang Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dar-Shyang Lee. 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 Dar-Shyang Lee. The network helps show where Dar-Shyang Lee may publish in the future.

Co-authorship network of co-authors of Dar-Shyang Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Dar-Shyang Lee. A scholar is included among the top collaborators of Dar-Shyang Lee based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Dar-Shyang Lee. Dar-Shyang Lee is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Schwarcz, Steven L., et al.. (2020). Adapting Style and Content for Attended Text Sequence Recognition. 17. 1586–1595. 1 indexed citations
2.
Lee, Dar-Shyang & Ray Smith. (2012). Improving Book OCR by Adaptive Language and Image Models. 115–119. 7 indexed citations
3.
Smith, Ray, et al.. (2009). Adapting the Tesseract open source OCR engine for multilingual OCR. 1–8. 75 indexed citations
4.
Lee, Dar-Shyang. (2005). Effective Gaussian mixture learning for video background subtraction. IEEE Transactions on Pattern Analysis and Machine Intelligence. 27(5). 827–832. 614 indexed citations breakdown →
5.
Hull, J.J., B. Erol, Jamey Graham, & Dar-Shyang Lee. (2004). Visualizing multimedia content on paper documents: components of key frame selection for Video Paper. 1. 389–392. 4 indexed citations
6.
Lee, Dar-Shyang, J.J. Hull, & B. Erol. (2004). A Bayesian framework for Gaussian mixture background modeling. 2. III–973. 79 indexed citations
7.
Erol, B., J.J. Hull, Jamey Graham, & Dar-Shyang Lee. (2004). Prescient paper: multimedia document creation with document image matching. Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.. 675–678 Vol.2. 1 indexed citations
8.
Hull, Jonathan J., et al.. (2003). Linking multimedia presentations with their symbolic source documents. 498–507. 20 indexed citations
9.
Graham, Jamey, et al.. (2003). The video paper multimedia playback system.
10.
Graham, Jamey, et al.. (2003). The video paper multimedia playback system. 94–95. 10 indexed citations
11.
Hull, Jonathan J., et al.. (2003). Linking multimedia presentations with their symbolic source documents. 8 indexed citations
12.
Lee, Dar-Shyang. (2002). Improved Adaptive Mixture Learning for Robust Video Background Modeling.. Machine Vision and Applications. 443–446. 13 indexed citations
13.
Lee, Dar-Shyang & Sargur N. Srihari. (2002). A theory of classifier combination: the neural network approach. 1. 42–45. 54 indexed citations
14.
Hull, J.J. & Dar-Shyang Lee. (2002). Simultaneous highlighting of paper and electronic documents. 4. 401–404. 1 indexed citations
15.
Srihari, Sargur N., et al.. (2002). Name and Address Block Reader system for tax form processing. 1. 5–10. 10 indexed citations
16.
Lee, Dar-Shyang, et al.. (2002). Comparison of normalization methods for character recognition. 2. 719–722. 13 indexed citations
17.
Lee, Dar-Shyang, et al.. (2002). Portable meeting recorder. 493–502. 49 indexed citations
18.
Lee, Dar-Shyang & Jonathan J. Hull. (2001). Detecting duplicates among symbolically compressed images in a large document database. Pattern Recognition Letters. 22(5). 545–550. 5 indexed citations
19.
Lee, Dar-Shyang & Sargur Srihari. (1995). <title>Dynamic classifier combination using neural network</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 2422. 26–37. 4 indexed citations
20.
Lee, Dar-Shyang, et al.. (1988). Neural network models and their application to handwritten digit recognition.. 63–70. 12 indexed citations

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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