Takeshi Agui

152 total papers · 595 total citations
49 papers, 443 citations indexed

About

Takeshi Agui is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Artificial Intelligence. According to data from OpenAlex, Takeshi Agui has authored 49 papers receiving a total of 443 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Computer Vision and Pattern Recognition, 9 papers in Computational Mechanics and 6 papers in Artificial Intelligence. Recurrent topics in Takeshi Agui's work include Advanced Numerical Analysis Techniques (6 papers), Computer Graphics and Visualization Techniques (5 papers) and Image and Object Detection Techniques (5 papers). Takeshi Agui is often cited by papers focused on Advanced Numerical Analysis Techniques (6 papers), Computer Graphics and Visualization Techniques (5 papers) and Image and Object Detection Techniques (5 papers). Takeshi Agui collaborates with scholars based in Japan, Brazil and United States. Takeshi Agui's co-authors include Masayuki Nakajima, Hiroshi Nagahashi, Masayuki Nakajima, Tomoharu Nagao, Masayuki Nakajima, Yasuaki Teramachi, Hiroki Takahashi, Tae‐Kyun Kim, Kiyoshi Arai and Hee-Dong Lee and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEICE Transactions on Information and Systems and IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences.

In The Last Decade

Takeshi Agui

44 papers receiving 390 citations

Author Peers

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

Author						Papers	Cites
Takeshi Agui	306	217	53	47	38	49	443
Michael D. Adams	346 1.1×	161 0.7×	44 0.8×	10 0.2×	59 1.6×	51	466
Yoonsik Choe	254 0.8×	126 0.6×	44 0.8×	53 1.1×	10 0.3×	89	372
Michael Gormish	411 1.3×	160 0.7×	49 0.9×	23 0.5×	7 0.2×	25	467
K. Wiatr	145 0.5×	76 0.4×	109 2.1×	101 2.1×	76 2.0×	87	385
Zihan Liu	115 0.4×	60 0.3×	127 2.4×	49 1.0×	23 0.6×	46	392
A. Benkrid	149 0.5×	69 0.3×	91 1.7×	90 1.9×	86 2.3×	31	371
Jau‐Yien Lee	209 0.7×	149 0.7×	180 3.4×	69 1.5×	36 0.9×	37	451
Oleksiy Koval	407 1.3×	67 0.3×	60 1.1×	72 1.5×	12 0.3×	74	483
Hyuk-Jae Lee	168 0.5×	49 0.2×	42 0.8×	157 3.3×	83 2.2×	60	407
Dongming Zhang	300 1.0×	76 0.4×	131 2.5×	25 0.5×	11 0.3×	64	450

Countries citing papers authored by Takeshi Agui

Since Specialization

Citations

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

Fields of papers citing papers by Takeshi Agui

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Takeshi Agui

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