Gee-Wah Ng

588 total citations
14 papers, 391 citations indexed

About

Gee-Wah Ng is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Media Technology. According to data from OpenAlex, Gee-Wah Ng has authored 14 papers receiving a total of 391 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 3 papers in Media Technology. Recurrent topics in Gee-Wah Ng's work include Image Retrieval and Classification Techniques (5 papers), Advanced Image and Video Retrieval Techniques (3 papers) and Reinforcement Learning in Robotics (3 papers). Gee-Wah Ng is often cited by papers focused on Image Retrieval and Classification Techniques (5 papers), Advanced Image and Video Retrieval Techniques (3 papers) and Reinforcement Learning in Robotics (3 papers). Gee-Wah Ng collaborates with scholars based in Singapore, United States and Hong Kong. Gee-Wah Ng's co-authors include Keng Teck, Xiaogang Wang, W. Eric L. Grimson, Ah‐Hwee Tan, Huiqing Chong, Kezhi Mao, Budhitama Subagdja, Di Wang, Dongzhe Wang and Tien Pham and has published in prestigious journals such as International Journal of Computer Vision, Neurocomputing and Artificial Intelligence Review.

In The Last Decade

Gee-Wah Ng

14 papers receiving 373 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gee-Wah Ng Singapore 7 258 228 79 47 20 14 391
Keng Teck Singapore 8 238 0.9× 323 1.4× 80 1.0× 41 0.9× 25 1.3× 14 418
Jagannadan Varadarajan Singapore 11 232 0.9× 324 1.4× 81 1.0× 46 1.0× 22 1.1× 28 472
Yiyu Qiu Australia 4 263 1.0× 259 1.1× 26 0.3× 11 0.2× 5 0.3× 6 489
Chaoyou Fu China 10 129 0.5× 350 1.5× 44 0.6× 11 0.2× 4 0.2× 25 536
Wankou Yang China 13 231 0.9× 382 1.7× 37 0.5× 16 0.3× 3 0.1× 32 522
Jesús Bescós Spain 11 107 0.4× 363 1.6× 95 1.2× 8 0.2× 7 0.3× 52 497
Amin Shoukry Egypt 12 154 0.6× 217 1.0× 29 0.4× 9 0.2× 6 0.3× 42 425
Juntae Kim South Korea 11 180 0.7× 88 0.4× 178 2.3× 7 0.1× 12 0.6× 43 382
Changlin Li China 9 140 0.5× 374 1.6× 30 0.4× 14 0.3× 9 0.5× 32 531
Uiwon Hwang South Korea 8 146 0.6× 141 0.6× 47 0.6× 8 0.2× 5 0.3× 13 327

Countries citing papers authored by Gee-Wah Ng

Since Specialization
Citations

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

Fields of papers citing papers by Gee-Wah Ng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gee-Wah Ng. 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 Gee-Wah Ng. The network helps show where Gee-Wah Ng may publish in the future.

Co-authorship network of co-authors of Gee-Wah Ng

This figure shows the co-authorship network connecting the top 25 collaborators of Gee-Wah Ng. A scholar is included among the top collaborators of Gee-Wah Ng 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 Gee-Wah Ng. Gee-Wah Ng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

14 of 14 papers shown
1.
2.
Mao, Kezhi, et al.. (2018). Feature Regrouping for CCA - Based Feature Fusion and Extraction Through Normalized Cut. 2275–2282. 11 indexed citations
3.
4.
Wang, Dongzhe, Kezhi Mao, & Gee-Wah Ng. (2017). Convolutional neural networks and multimodal fusion for text aided image classification. 1–7. 13 indexed citations
5.
Wang, Dongzhe, Kezhi Mao, Gee-Wah Ng, & Tien Pham. (2016). Adaptive multimodal fusion with web resources for scene classification. International Conference on Information Fusion. 114–121. 3 indexed citations
6.
Wang, Dongzhe, Kezhi Mao, & Gee-Wah Ng. (2015). Improving scene classification by fusion of training data and web resources. International Conference on Information Fusion. 823–829. 4 indexed citations
7.
Wang, Xiaogang, Keng Teck, Gee-Wah Ng, & W. Eric L. Grimson. (2011). Trajectory Analysis and Semantic Region Modeling Using Nonparametric Hierarchical Bayesian Models. International Journal of Computer Vision. 95(3). 287–312. 123 indexed citations
8.
9.
Ng, Gee-Wah. (2009). Brain-Mind Machinery. WORLD SCIENTIFIC eBooks. 1 indexed citations
10.
Wang, Di, Budhitama Subagdja, Ah‐Hwee Tan, & Gee-Wah Ng. (2009). Creating Human-like Autonomous Players in Real-time First Person Shooter Computer Games. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 173. 35 indexed citations
11.
Ng, Gee-Wah. (2009). Brain-Mind Machinery - Brain-Inspired Computing and Mind Opening. World Scientific Publishing Co. Pte. Ltd. eBooks. 5 indexed citations
12.
Wang, Xiaogang, Keng Teck, Gee-Wah Ng, & W. Eric L. Grimson. (2008). Trajectory analysis and semantic region modeling using a nonparametric Bayesian model. DSpace@MIT (Massachusetts Institute of Technology). 1–8. 121 indexed citations
13.
Chong, Huiqing, Ah‐Hwee Tan, & Gee-Wah Ng. (2007). Integrated cognitive architectures: a survey. Artificial Intelligence Review. 28(2). 103–130. 55 indexed citations
14.
Ng, Gee-Wah, et al.. (2003). Sensor management: coverage versus survivability - a multiple-objective optimization problem. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 5099. 421–421. 1 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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