Jun Toyama

517 citations
29 papers · 298 · h-index 9

Impact in

Papers in

Jun Toyama

25 papers receiving 293 citations

Peers

Jun Toyama
Comparison fields: 5 of 89
  • Computer Vision and Pattern Recognition 80
  • Medical Laboratory Technology 5
  • Human-Computer Interaction 20
  • Signal Processing 39
  • Artificial Intelligence 85
Replace Mohammad Ashfak Habib with:
Mohammad Ashfak Habib Bangladesh
Anton Čižmár Slovakia
Sandra Ebert Germany
Chengcheng Jia China
Ziad Al-Halah Germany
Diego Tosato Italy
Samyak Jain India
Gregorio Palmas Germany
Jakeoung Koo South Korea
Varun Raj Kompella Switzerland
Jun Toyama relative to Mohammad Ashfak Habib Bangladesh Mohammad Ashfak Habib's profile →
Citations per field
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Mohammad Ashfak Habib · 1×
Citations per year

Countries citing papers authored by Jun Toyama

Since Specialization
Citations

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

Fields of papers citing papers by Jun Toyama

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 18 scholars most cited alongside Jun Toyama, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jun Toyama Line = papers co-authored together Jun Toyama links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 29 papers — load more, or switch the sort, to bring in the rest.

#Work
1 199993
2 200843
3 202340
4 201423
5 200921
6 200612
7 200910
8 20119
9 20118
10 20065
11 20034
12 20124
13
Person authentication and activities analysis in an office environment using a sensor network
20123
14
Construction of nonlinear discrimination function based on the MDL criterion.
19983
15 20013
16 20113
17
Knowledge-based enhancement of low spatial resolution images
19982
18 20122
19 20112
20 20142

About Jun Toyama

Jun Toyama is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing, Electrical and Electronic Engineering and Statistics and Probability, having authored 29 papers that have together received 298 indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (6 papers), Statistical Methods and Bayesian Inference (4 papers), Statistical Methods and Inference (4 papers), Gait Recognition and Analysis (4 papers), Human Pose and Action Recognition (3 papers), IoT-based Smart Home Systems (3 papers), Ergonomics and Musculoskeletal Disorders (3 papers) and Anomaly Detection Techniques and Applications (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (80 citations), Medical Laboratory Technology (5 citations), Human-Computer Interaction (20 citations), Signal Processing (39 citations) and Artificial Intelligence (85 citations). Jun Toyama has collaborated with scholars based in Japan and China. Frequent co-authors include Mineichi Kudo, Masaru Shimbo, Shuai Tao, Hideyuki Imai, Hiroaki Kobayashi, Masao Omata, Toshiharu Tsutsui, Guoliang Lu, Akitoshi Saito and Yumiko Kakizaki. Their work appears in journals such as Pattern Recognition Letters, Journal of Multivariate Analysis, The Journal of the Acoustical Society of America, Pattern Analysis and Applications and Med.

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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