Shan Fu

158 total papers · 833 total citations
65 papers, 542 citations indexed

About

Shan Fu is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Social Psychology. According to data from OpenAlex, Shan Fu has authored 65 papers receiving a total of 542 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Computer Vision and Pattern Recognition, 16 papers in Aerospace Engineering and 12 papers in Social Psychology. Recurrent topics in Shan Fu's work include Advanced Vision and Imaging (12 papers), Human-Automation Interaction and Safety (11 papers) and Video Surveillance and Tracking Methods (10 papers). Shan Fu is often cited by papers focused on Advanced Vision and Imaging (12 papers), Human-Automation Interaction and Safety (11 papers) and Video Surveillance and Tracking Methods (10 papers). Shan Fu collaborates with scholars based in China, Canada and United Kingdom. Shan Fu's co-authors include Lu Ding, Dan Huang, Lei Song, Yong Wang, Rob Law, Bo Jia, Biting Yu, Jiayang Huang, Yingping Huang and Chris Thompson and has published in prestigious journals such as IEEE Transactions on Industrial Electronics, IEEE Access and Sustainability.

In The Last Decade

Shan Fu

61 papers receiving 523 citations

Author Peers

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

Author						Papers	Cites
Shan Fu	184	112	97	92	68	65	542
Hongsheng He	194 1.1×	57 0.5×	47 0.5×	57 0.6×	130 1.9×	65	617
Jan Fischer	380 2.1×	147 1.3×	34 0.4×	38 0.4×	54 0.8×	54	565
Yuki Uranishi	216 1.2×	73 0.7×	26 0.3×	58 0.6×	59 0.9×	61	466
Haris Baltzakis	369 2.0×	147 1.3×	72 0.7×	34 0.4×	23 0.3×	26	516
Christopher R. Hudson	125 0.7×	100 0.9×	19 0.2×	80 0.9×	52 0.8×	42	587
Frédéric Lerasle	383 2.1×	195 1.7×	24 0.2×	67 0.7×	33 0.5×	59	613
Vladan Papić	184 1.0×	85 0.8×	33 0.3×	26 0.3×	90 1.3×	81	487
Daniele Pannone	319 1.7×	134 1.2×	36 0.4×	28 0.3×	39 0.6×	26	522
Sei-Wang Chen	255 1.4×	34 0.3×	53 0.5×	37 0.4×	38 0.6×	38	488
Lining Sun	228 1.2×	36 0.3×	143 1.5×	28 0.3×	139 2.0×	48	595

Countries citing papers authored by Shan Fu

Since Specialization

Citations

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

Fields of papers citing papers by Shan Fu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shan Fu

This figure shows the co-authorship network connecting the top 25 collaborators of Shan Fu. A scholar is included among the top collaborators of Shan Fu 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 Shan Fu. Shan Fu 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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