Hu Lu
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Cognitive Neuroscience top 10%
- Electrical and Electronic Engineering
- Computer Networks and Communications top 10%
- Co-authors
- Q.T. ZhangHui WeiYuqing SongShaohua WanPingping ZhangZhe LiuShang GaoDong Wang
- Topics
- Functional Brain Connectivity Studies (9 papers)Video Surveillance and Tracking Methods (7 papers)Complex Network Analysis Techniques (6 papers)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Hu Lu
46 papers receiving 656 citations
Peers
Comparison fields: 5 of 93
- Computer Vision and Pattern Recognition 276
- Artificial Intelligence 169
- Cognitive Neuroscience 130
- Electrical and Electronic Engineering 119
- Computer Networks and Communications 95
Countries citing papers authored by Hu Lu
This map shows the geographic impact of Hu Lu'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 Hu Lu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hu Lu more than expected).
Fields of papers citing papers by Hu Lu
This network shows the impact of papers produced by Hu Lu. 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 Hu Lu. The network helps show where Hu Lu may publish in the future.
Co-authorship network of co-authors of Hu Lu
This figure shows the co-authorship network connecting the top 25 collaborators of Hu Lu. A scholar is included among the top collaborators of Hu Lu 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 Hu Lu. Hu Lu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 0 | |
| 7 | 10 | |
| 8 | 2 | |
| 9 | 4 | |
| 10 | 1 | |
| 11 | 0 | |
| 12 | 15 | |
| 13 | 26 | |
| 14 | 24 | |
| 15 | Recognition of crop seedling and weed recognition based on dilated convolution and global pooling in CNN. | 12 |
| 16 | Partitioning the Firing Patterns of Spike Trains by Community Modularity. | 1 |
| 17 | 1 | |
| 18 | 9 | |
| 19 | 8 | |
| 20 | Intrusion Detection Based on PCA and Feature-weighted Fuzzy Clustering | 1 |
About Hu Lu
Hu Lu is a scholar working on Computer Vision and Pattern Recognition, Urban Studies and Media Technology, having authored 52 papers that have together received 668 indexed citations. Recurring topics across this work include Functional Brain Connectivity Studies (9 papers), Video Surveillance and Tracking Methods (7 papers) and Complex Network Analysis Techniques (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (276 citations), Computational Mathematics (5 citations) and Cognitive Neuroscience (130 citations). Hu Lu has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Q.T. Zhang, Hui Wei, Yuqing Song, Shaohua Wan, Pingping Zhang, Zhe Liu, Shang Gao, Dong Wang, Yirui Wu and Zonghua Gu. Their work appears in journals such as PLoS ONE, IEEE Transactions on Image Processing and Expert Systems with Applications.
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.