T. Kaneko
- Computer Vision and Pattern Recognition top 5%
- Signal Processing top 10%
- Nuclear and High Energy Physics top 10%
- Artificial Intelligence top 10%
- Media Technology top 5%
- Topics
- Astrophysics and Cosmic Phenomena (19 papers)Dark Matter and Cosmic Phenomena (9 papers)Medical Image Segmentation Techniques (8 papers)
- Journals
- Proceedings of the IEEEInternational Journal of Remote SensingJournal of the Physical Society of Japan
- Partner nations
- JapanUnited StatesBolivia
In The Last Decade
T. Kaneko
46 papers receiving 719 citations
Peers
Comparison fields: 5 of 104
- Computer Vision and Pattern Recognition 295
- Signal Processing 98
- Nuclear and High Energy Physics 95
- Artificial Intelligence 92
- Media Technology 83
Countries citing papers authored by T. Kaneko
This map shows the geographic impact of T. Kaneko'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 T. Kaneko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T. Kaneko more than expected).
Fields of papers citing papers by T. Kaneko
This network shows the impact of papers produced by T. Kaneko. 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 T. Kaneko. The network helps show where T. Kaneko may publish in the future.
Co-authorship network of co-authors of T. Kaneko
This figure shows the co-authorship network connecting the top 25 collaborators of T. Kaneko. A scholar is included among the top collaborators of T. Kaneko 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 T. Kaneko. T. Kaneko is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 9 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 6 | |
| 5 | 43 | |
| 6 | 8 | |
| 7 | 3 | |
| 8 | 5 | |
| 9 | Search for GeV Gamma Ray Bursts at Mount Chacaltaya | 2 |
| 10 | Search for gamma ray bursts at Chacaltaya | 1 |
| 11 | 3 | |
| 12 | Composition and Energy Spectrum of Cosmic Rays above 30 TeV | 0 |
| 13 | New Bolivia Solar Neutron Telescope | 1 |
| 14 | 5 | |
| 15 | 21 | |
| 16 | 2 | |
| 17 | 291 | |
| 18 | Boundary Detection of Radiographic Images by a Threshold Method. | 15 |
| 19 | 6 | |
| 20 | 2 |
About T. Kaneko
T. Kaneko is a scholar working on Computer Graphics and Computer-Aided Design, Nuclear and High Energy Physics and Computer Vision and Pattern Recognition, having authored 50 papers that have together received 790 indexed citations. Recurring topics across this work include Astrophysics and Cosmic Phenomena (19 papers), Dark Matter and Cosmic Phenomena (9 papers) and Medical Image Segmentation Techniques (8 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (295 citations), Media Technology (83 citations) and Signal Processing (98 citations). T. Kaneko has collaborated with scholars based in Japan, United States and Bolivia. Frequent co-authors include C. K. Chow, B. Liu, Takeshi Saitoh, N. Dixon, K. Hinotani, S. Miyake, Koichi Hirota, Atsuko Tanaka, David A. Rothery and Martin J. Wooster. Their work appears in journals such as Proceedings of the IEEE, International Journal of Remote Sensing and Journal of the Physical Society of Japan.
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.