Yakun Ju

66 total papers · 851 total citations
43 papers, 543 citations indexed

About

Yakun Ju is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Computational Mechanics. According to data from OpenAlex, Yakun Ju has authored 43 papers receiving a total of 543 indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Computer Vision and Pattern Recognition, 13 papers in Computer Graphics and Computer-Aided Design and 11 papers in Computational Mechanics. Recurrent topics in Yakun Ju's work include Advanced Vision and Imaging (23 papers), Computer Graphics and Visualization Techniques (13 papers) and 3D Shape Modeling and Analysis (11 papers). Yakun Ju is often cited by papers focused on Advanced Vision and Imaging (23 papers), Computer Graphics and Visualization Techniques (13 papers) and 3D Shape Modeling and Analysis (11 papers). Yakun Ju collaborates with scholars based in China, Hong Kong and United Kingdom. Yakun Ju's co-authors include Junyu Dong, Kin‐Man Lam, Lin Qi, Muwei Jian, Cong Zhang, Boxin Shi, Sheng Chen, Qi Wang, Jingran Su and Feng Gao and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Geoscience and Remote Sensing and IEEE Transactions on Image Processing.

In The Last Decade

Yakun Ju

41 papers receiving 535 citations

Author Peers

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

Author						Papers	Cites
Yakun Ju	405	140	104	92	89	43	543
André Gagalowicz	415 1.0×	178 1.3×	95 0.9×	38 0.4×	63 0.7×	44	570
Kartic Subr	374 0.9×	245 1.8×	128 1.2×	47 0.5×	53 0.6×	44	581
Jordi Salvador	399 1.0×	103 0.7×	107 1.0×	36 0.4×	153 1.7×	29	502
Luc J. Van Gool	392 1.0×	46 0.3×	56 0.5×	65 0.7×	46 0.5×	41	498
Pengfei Xu	311 0.8×	97 0.7×	118 1.1×	24 0.3×	97 1.1×	39	507
R.L. Carceroni	421 1.0×	76 0.5×	38 0.4×	151 1.6×	32 0.4×	28	518
Guido M. Cortelazzo	398 1.0×	33 0.2×	63 0.6×	111 1.2×	37 0.4×	40	574
Pan Gao	338 0.8×	102 0.7×	119 1.1×	16 0.2×	53 0.6×	62	463
Jie Li	458 1.1×	34 0.2×	66 0.6×	98 1.1×	48 0.5×	39	618
Giovanni Pintore	309 0.8×	95 0.7×	32 0.3×	113 1.2×	71 0.8×	58	501

Countries citing papers authored by Yakun Ju

Since Specialization

Citations

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

Fields of papers citing papers by Yakun Ju

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yakun Ju

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