Dar-Shyang Lee

1.5k citations
22 papers · 1.0k indexed · 1 hit paper · h-index 11

Dar-Shyang Lee

21 papers receiving 947 citations

Hit Papers

Effective Gaussian mixture learning for video background ...6142005202620122019200400600

Peers

Dar-Shyang Lee
Comparison fields: 5 of 87
  • Computer Vision and Pattern Recognition 856
  • Media Technology 114
  • Signal Processing 89
  • Safety, Risk, Reliability and Quality 66
  • Artificial Intelligence 222
Replace Yao Lu with:
Yao Lu China
Zhenjiang Miao China
De Cheng China
Rin-ichiro Taniguchi Japan
Benjamin Höferlin Germany
Supavadee Aramvith Thailand
Atousa Torabi Canada
Dehui Kong China
Hefei Ling China
Hai Min China
Dar-Shyang Lee relative to Yao Lu China Yao Lu's profile →
Citations per field
00.5×1.7×
Yao Lu · 1×
Citations per year

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

The 10 scholars most cited alongside Dar-Shyang Lee, 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 Dar-Shyang Lee Line = papers co-authored together Dar-Shyang Lee links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20201
2 20127
3 200975
4
Effective Gaussian mixture learning for video background subtractionbreakdown →
2005614
5 200479
6 20044
7 20041
8 200320
9 20030
10 200310
11 20038
12
Improved Adaptive Mixture Learning for Robust Video Background Modeling.
200213
13 200210
14 20021
15 200254
16 200213
17 20024
18 20015
19 19954
20
Neural network models and their application to handwritten digit recognition.
198812

About Dar-Shyang Lee

Dar-Shyang Lee is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Media Technology, having authored 22 papers that have together received 1.0k indexed citations. Recurring topics across this work include Handwritten Text Recognition Techniques (9 papers), Video Analysis and Summarization (8 papers), Music and Audio Processing (6 papers), Multimedia Communication and Technology (4 papers), Video Surveillance and Tracking Methods (3 papers), Natural Language Processing Techniques (3 papers), Speech and Audio Processing (2 papers) and Image Processing and 3D Reconstruction (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (856 citations), Media Technology (114 citations) and Signal Processing (89 citations). Dar-Shyang Lee has collaborated with scholars based in United States and Japan. Frequent co-authors include Sargur N. Srihari, Ray Smith, Jonathan J. Hull, B. Erol, J.J. Hull, Jamey Graham, Yongchul Shin, Sargur Srihari, Steven L. Schwarcz and Alexander N. Gorban. Their work appears in journals such as Machine Vision and Applications, Pattern Recognition Letters, Proceedings of the IEEE, IEEE Transactions on Pattern Analysis and Machine Intelligence and Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE.

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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