Na Han

44 total papers · 1.1k total citations
33 papers, 784 citations indexed

About

Na Han is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Mechanics. According to data from OpenAlex, Na Han has authored 33 papers receiving a total of 784 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Computer Vision and Pattern Recognition, 15 papers in Artificial Intelligence and 12 papers in Computational Mechanics. Recurrent topics in Na Han's work include Face and Expression Recognition (13 papers), Sparse and Compressive Sensing Techniques (12 papers) and Remote-Sensing Image Classification (10 papers). Na Han is often cited by papers focused on Face and Expression Recognition (13 papers), Sparse and Compressive Sensing Techniques (12 papers) and Remote-Sensing Image Classification (10 papers). Na Han collaborates with scholars based in China, Hong Kong and Australia. Na Han's co-authors include Xiaozhao Fang, Jie Wen, Jigang Wu, Yong Xu, Shanhua Zhan, Jian Yang, Wai Keung Wong, Shengli Xie, Shaohua Teng and Lunke Fei and has published in prestigious journals such as IEEE Transactions on Image Processing, Pattern Recognition and IEEE Transactions on Cybernetics.

In The Last Decade

Na Han

29 papers receiving 780 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Na Han	555	327	189	175	50	33	784
Hao Wang	516 0.9×	434 1.3×	115 0.6×	68 0.4×	26 0.5×	22	816
Kai Zhang	564 1.0×	258 0.8×	173 0.9×	101 0.6×	31 0.6×	14	742
Bin Shen	443 0.8×	215 0.7×	89 0.5×	112 0.6×	24 0.5×	51	687
Jingyuan Xia	548 1.0×	444 1.4×	162 0.9×	60 0.3×	43 0.9×	44	898
Jiajun Wen	458 0.8×	214 0.7×	116 0.6×	133 0.8×	20 0.4×	51	641
Xiaolin Xiao	660 1.2×	268 0.8×	175 0.9×	232 1.3×	10 0.2×	28	901
Quanxue Gao	628 1.1×	229 0.7×	206 1.1×	197 1.1×	21 0.4×	30	862
Weizhong Yu	404 0.7×	308 0.9×	183 1.0×	41 0.2×	19 0.4×	38	686
Weida Zhou	412 0.7×	340 1.0×	98 0.5×	170 1.0×	54 1.1×	41	813
Deyan Xie	587 1.1×	355 1.1×	146 0.8×	148 0.8×	23 0.5×	26	809

Countries citing papers authored by Na Han

Since Specialization

Citations

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

Fields of papers citing papers by Na Han

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Na Han

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

All Works

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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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