Takashi Ijiri
- Computer Graphics and Computer-Aided Design top 1%
- Computer Vision and Pattern Recognition top 5%
- Computational Mechanics top 5%
- Plant Science
- Environmental Engineering
- Co-authors
- Takeo IgarashiMakoto OkabeShigeru OwadaYuki IgarashiHideo YokotaKenshi TakayamaRadomír MěchGavin Miller
- Topics
- Computer Graphics and Visualization Techniques (17 papers)3D Shape Modeling and Analysis (12 papers)Sparse and Compressive Sensing Techniques (6 papers)
- Cited by
- Computer Graphics and Computer-Aided DesignComputer Vision and Pattern RecognitionComputational Mechanics
- Partner nations
- JapanUnited StatesSweden
In The Last Decade
Takashi Ijiri
42 papers receiving 499 citations
Peers
Comparison fields: 5 of 91
- Computer Graphics and Computer-Aided Design 231
- Computer Vision and Pattern Recognition 194
- Computational Mechanics 160
- Plant Science 113
- Environmental Engineering 62
Countries citing papers authored by Takashi Ijiri
This map shows the geographic impact of Takashi Ijiri'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 Takashi Ijiri with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Takashi Ijiri more than expected).
Fields of papers citing papers by Takashi Ijiri
This network shows the impact of papers produced by Takashi Ijiri. 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 Takashi Ijiri. The network helps show where Takashi Ijiri may publish in the future.
Co-authorship network of co-authors of Takashi Ijiri
This figure shows the co-authorship network connecting the top 25 collaborators of Takashi Ijiri. A scholar is included among the top collaborators of Takashi Ijiri 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 Takashi Ijiri. Takashi Ijiri is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 14 | |
| 8 | High-Quality MR Imaging Based on Dictionary Learning Using Training Images and Observed Signals | 2 |
| 9 | 9 | |
| 10 | 4 | |
| 11 | 6 | |
| 12 | 0 | |
| 13 | 1 | |
| 14 | 8 | |
| 15 | 10 | |
| 16 | 4 | |
| 17 | Surface-based growth simulation for opening flowers | 19 |
| 18 | 68 | |
| 19 | 10 | |
| 20 | Estimation of Yield Loss and Computerized Forecasting System (Blightas) for Rice Sheath Blight Disease | 5 |
About Takashi Ijiri
Takashi Ijiri is a scholar working on Computer Graphics and Computer-Aided Design, Computational Mechanics and Computer Vision and Pattern Recognition, having authored 49 papers that have together received 528 indexed citations. Recurring topics across this work include Computer Graphics and Visualization Techniques (17 papers), 3D Shape Modeling and Analysis (12 papers) and Sparse and Compressive Sensing Techniques (6 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (231 citations), Computer Vision and Pattern Recognition (194 citations) and Computational Mechanics (160 citations). Takashi Ijiri has collaborated with scholars based in Japan, United States and Sweden. Frequent co-authors include Takeo Igarashi, Makoto Okabe, Shigeru Owada, Yuki Igarashi, Hideo Yokota, Kenshi Takayama, Radomír Měch, Gavin Miller, Shigeki Owada and S. Yoshizawa. Their work appears in journals such as PLoS ONE, ACM Transactions on Graphics and Computer Graphics Forum.
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.