Di Xu

60 total papers · 421 total citations
35 papers, 251 citations indexed

About

Di Xu is a scholar working on Computer Vision and Pattern Recognition, Education and Media Technology. According to data from OpenAlex, Di Xu has authored 35 papers receiving a total of 251 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Computer Vision and Pattern Recognition, 5 papers in Education and 4 papers in Media Technology. Recurrent topics in Di Xu's work include Advanced Vision and Imaging (11 papers), Image Enhancement Techniques (4 papers) and Computer Graphics and Visualization Techniques (4 papers). Di Xu is often cited by papers focused on Advanced Vision and Imaging (11 papers), Image Enhancement Techniques (4 papers) and Computer Graphics and Visualization Techniques (4 papers). Di Xu collaborates with scholars based in China, Canada and Singapore. Di Xu's co-authors include Shanna Smith Jaggars, Panos Nasiopoulos, Michael D. Adams, Qi Cao, Yigang Cen, Juyong Zhang, Jianfei Cai, Qi Duan, Tat‐Jen Cham and Jiwat Ram and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Medical Imaging and Journal of Magnetic Resonance Imaging.

In The Last Decade

Di Xu

31 papers receiving 239 citations

Author Peers

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

Author						Papers	Cites
Di Xu	98	76	30	24	22	35	251
Athanasios Manitsaris	80 0.8×	29 0.4×	28 0.9×	18 0.8×	27 1.2×	39	325
Ruzinoor Che Mat	57 0.6×	102 1.3×	14 0.5×	7 0.3×	39 1.8×	34	301
Howard Besser	87 0.9×	74 1.0×	28 0.9×	7 0.3×	25 1.1×	38	329
Yannick Prié	137 1.4×	29 0.4×	19 0.6×	4 0.2×	50 2.3×	32	286
Michael Ashley	59 0.6×	96 1.3×	13 0.4×	24 1.0×	21 1.0×	19	286
Thibault Carron	120 1.2×	25 0.3×	59 2.0×	41 1.7×	79 3.6×	30	271
Gonzalo Gabriel Méndez	127 1.3×	30 0.4×	88 2.9×	4 0.2×	35 1.6×	22	264
Anuja Dharmaratne	71 0.7×	18 0.2×	18 0.6×	6 0.3×	30 1.4×	37	212
Paraskevi Tzouveli	102 1.0×	24 0.3×	47 1.6×	7 0.3×	48 2.2×	30	303
Felipe Fernandes	64 0.7×	16 0.2×	6 0.2×	26 1.1×	4 0.2×	33	260

Countries citing papers authored by Di Xu

Since Specialization

Citations

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

Fields of papers citing papers by Di Xu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Di Xu

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