Hua Li

71 papers receiving 1.0k citations

Hit Papers

Nitrogen use efficiency in crops: lessons from Arabidopsi...2017202620202023201750100150200250

Peers

Hua Li
Comparison fields: 5 of 130
  • Computer Vision and Pattern Recognition 431
  • Plant Science 257
  • Radiology, Nuclear Medicine and Imaging 227
  • Artificial Intelligence 189
  • Computational Mechanics 94
Replace Morium Akter with:
Morium Akter Bangladesh
Umme Sara Bangladesh
Zunlei Feng China
Gady Agam United States
Zhiheng Wang China
Byung‐Woo Hong South Korea
Mark Eramian Canada
Nicolas Passat France
Mircea Cimpoi United Kingdom
Hua Li relative to Morium Akter Bangladesh Morium Akter's profile →
Citations per field
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Morium Akter · 1×
Citations per year

Countries citing papers authored by Hua Li

Since Specialization
Citations

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

Fields of papers citing papers by Hua Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hua Li. 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 Hua Li. The network helps show where Hua Li may publish in the future.

Co-authorship network of co-authors of Hua Li

This figure shows the co-authorship network connecting the top 25 collaborators of Hua Li. A scholar is included among the top collaborators of Hua Li 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 Hua Li. Hua Li 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
#WorkIndexed citations
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4 8
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Nitrogen use efficiency in crops: lessons from Arabidopsis and ricebreakdown →
277
13 2
14
3D Model Retrieval Based on 3D Fractional Fourier Transform
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15 21
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Three-dimensional reconstruction of human sciatic nerve from serial tissue sections
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Tracking of Players from Soccer Video Sequences
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About Hua Li

Hua Li is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Theoretical Computer Science, having authored 77 papers that have together received 1.1k indexed citations. Recurring topics across this work include Image Retrieval and Classification Techniques (21 papers), Advanced Image and Video Retrieval Techniques (15 papers) and 3D Shape Modeling and Analysis (11 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (431 citations), Computer Graphics and Computer-Aided Design (47 citations) and Radiology, Nuclear Medicine and Imaging (227 citations). Hua Li has collaborated with scholars based in China, United States and France. Frequent co-authors include Chengcai Chu, Bin Hu, Dong Xu, Anthony Yezzi, Dengao Li, Dong-Hui Xu, Yanyun Ma, Jumin Zhao, Zongmin Li and Chen Zhang. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Journal of Experimental Botany and The Journal of Physical Chemistry 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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