Atsuto Maki
About
In The Last Decade
Atsuto Maki
71 papers receiving 3.5k citations
Hit Papers
Peers
Comparison fields: 5 of 176
- Computer Vision and Pattern Recognition 1.5k
- Artificial Intelligence 1.3k
- Radiology, Nuclear Medicine and Imaging 427
- Biomedical Engineering 295
- Media Technology 273
Countries citing papers authored by Atsuto Maki
This map shows the geographic impact of Atsuto Maki'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 Atsuto Maki with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Atsuto Maki more than expected).
Fields of papers citing papers by Atsuto Maki
This network shows the impact of papers produced by Atsuto Maki. 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 Atsuto Maki. The network helps show where Atsuto Maki may publish in the future.
Co-authorship network of co-authors of Atsuto Maki
This figure shows the co-authorship network connecting the top 25 collaborators of Atsuto Maki. A scholar is included among the top collaborators of Atsuto Maki 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 Atsuto Maki. Atsuto Maki 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 | 4 | |
| 3 | 6 | |
| 4 | 8 | |
| 5 | Special Section on Machine Vision and its Applications FOREWORD | 0 |
| 6 | Feature Contraction: New ConvNet Regularization in Image Classification | 4 |
| 7 | A systematic study of the class imbalance problem in convolutional neural networks breakdown → | 1716 |
| 8 | 252 | |
| 9 | From Generic to Specific Deep Representations for Visual Recognition | 15 |
| 10 | A Baseline for Visual Instance Retrieval with Deep Convolutional Networks | 56 |
| 11 | Homogeneous Superpixels from Random Walks | 8 |
| 12 | 2D-to-3D photo rendering for 3D displays | 2 |
| 13 | 6 | |
| 14 | 10 | |
| 15 | 0 | |
| 16 | 13 | |
| 17 | Geotensity: Combining Motion and Lighting for 3D Surface Reconstruction | 1 |
| 18 | A Direct Disparity Estimation Technique for Depth Segmentation | 2 |
| 19 | Phase-Based Disparity Estimation in Binocular Tracking | 5 |
| 20 | 1 |
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.