Kazuki Kozuka
- Artificial Intelligence top 5%
- Anomaly Detection Techniques and Applications 2
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- Advanced Vision and Imaging 3
- Optical measurement and interference techniques 3
- Advanced Neural Network Applications 2
- Generative Adversarial Networks and Image Synthesis 1
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- Robotics and Sensor-Based Localization 3
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- Radiomics and Machine Learning in Medical Imaging 1
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- Cardiovascular Disease and Adiposity 1
- Co-authors
- Shun IshizakaDenis GudovskiyYoshihide SawadaJuan Carlos NieblesJun SatoKoji MorikawaRikiya YamashitaYasuhiro Honda
- Cited by
- Industrial and Manufacturing EngineeringArtificial IntelligenceComputer Vision and Pattern Recognition
- Journals
- Circulation (1 paper)International Journal of Automation and Computing (1 paper)International Journal of E-Health and Medical Communications (1 paper)
- Partner nations
- JapanUnited States
In The Last Decade
Kazuki Kozuka
8 papers receiving 327 citations
Hit Papers
Peers
Comparison fields: 5 of 45
- Industrial and Manufacturing Engineering 86
- Artificial Intelligence 258
- Computer Vision and Pattern Recognition 99
- Computer Networks and Communications 77
- Signal Processing 28
Countries citing papers authored by Kazuki Kozuka
This map shows the geographic impact of Kazuki Kozuka'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 Kazuki Kozuka with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kazuki Kozuka more than expected).
Fields of papers citing papers by Kazuki Kozuka
This network shows the impact of papers produced by Kazuki Kozuka. 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 Kazuki Kozuka. The network helps show where Kazuki Kozuka may publish in the future.
Co-authorship network
The 18 scholars most cited alongside Kazuki Kozuka, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 2 | |
| 2 | CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowsbreakdown → | 2022 | 300 |
| 3 | 2020 | 1 | |
| 4 | 2017 | 5 | |
| 5 | 2015 | 14 | |
| 6 | 2013 | 1 | |
| 7 | 2012 | 3 | |
| 8 | MULTIPLE VIEW GEOMETRY FOR MIXED DIMENSIONAL CAMERAS | 2008 | 3 |
| 9 | 2008 | 0 | |
| 10 | 2007 | 0 |
About Kazuki Kozuka
Kazuki Kozuka is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Aerospace Engineering, having authored 10 papers that have together received 329 indexed citations. Recurring topics across this work include Advanced Vision and Imaging (3 papers), Optical measurement and interference techniques (3 papers), Robotics and Sensor-Based Localization (3 papers), Anomaly Detection Techniques and Applications (2 papers), Advanced Neural Network Applications (2 papers), Radiomics and Machine Learning in Medical Imaging (1 paper), Cardiovascular Disease and Adiposity (1 paper) and Generative Adversarial Networks and Image Synthesis (1 paper). The work is most often cited by research in Industrial and Manufacturing Engineering (86 citations), Artificial Intelligence (258 citations) and Computer Vision and Pattern Recognition (99 citations). Kazuki Kozuka has collaborated with scholars based in Japan and United States. Frequent co-authors include Shun Ishizaka, Denis Gudovskiy, Yoshihide Sawada, Juan Carlos Niebles, Jun Sato, Koji Morikawa, Rikiya Yamashita, Yasuhiro Honda, Takayoshi Yamashita and Toyohiko Sakai. Their work appears in journals such as Circulation, International Journal of Automation and Computing and International Journal of E-Health and Medical Communications.
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.