Daniel DeTone
Impact in
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- Advanced Image and Video Retrieval Techniques
- Advanced Vision and Imaging
- Advanced Neural Network Applications
- Robotic Path Planning Algorithms
- Video Surveillance and Tracking Methods
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- 3D Surveying and Cultural Heritage
Papers in
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- Advanced Image and Video Retrieval Techniques 4
- Advanced Vision and Imaging 3
- Advanced Neural Network Applications 2
- Video Surveillance and Tracking Methods 1
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- Robotics and Sensor-Based Localization 5
- Co-authors
- Tomasz Malisiewicz (2 shared papers)Richard Newcombe (5 shared papers)Tsun-Yi Yang (3 shared papers)Julian Straub (4 shared papers)Paul-Edouard Sarlin (1 shared paper)Chris Sweeney (3 shared papers)Peter Kontschieder (1 shared paper)Samuel Rota Bulò (1 shared paper)
- Journals
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2 papers)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)2021 IEEE/CVF International Conference on Computer Vision (ICCV) (1 paper)
- Partner nations
- United StatesIsraelAustralia
In The Last Decade
Daniel DeTone
6 papers receiving 156 citations
Peers
Comparison fields: 5 of 45
- Computer Vision and Pattern Recognition 117
- Geology 28
- Aerospace Engineering 89
- Human-Computer Interaction 6
- Instrumentation 3
Countries citing papers authored by Daniel DeTone
This map shows the geographic impact of Daniel DeTone'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 Daniel DeTone with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel DeTone more than expected).
Fields of papers citing papers by Daniel DeTone
This network shows the impact of papers produced by Daniel DeTone. 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 Daniel DeTone. The network helps show where Daniel DeTone may publish in the future.
Co-authors
The 25 scholars most cited alongside Daniel DeTone, 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 | 2019 | 59 | |
| 2 | 2023 | 52 | |
| 3 | 2022 | 19 | |
| 4 | 2021 | 17 | |
| 5 | 2022 | 9 | |
| 6 | 2022 | 5 | |
| 7 | 2025 | 0 | |
| 8 | 2022 | 0 |
About Daniel DeTone
Daniel DeTone is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering, Artificial Intelligence, Geology and Infectious Diseases, having authored 8 papers that have together received 161 indexed citations. Recurring topics across this work include Robotics and Sensor-Based Localization (5 papers), Advanced Image and Video Retrieval Techniques (4 papers), Advanced Vision and Imaging (3 papers), Advanced Neural Network Applications (2 papers), 3D Surveying and Cultural Heritage (1 paper), Machine Learning and Algorithms (1 paper), Intelligent Tutoring Systems and Adaptive Learning (1 paper) and Video Surveillance and Tracking Methods (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (117 citations), Geology (28 citations), Aerospace Engineering (89 citations), Human-Computer Interaction (6 citations) and Instrumentation (3 citations). Daniel DeTone has collaborated with scholars based in United States, Israel and Australia. Frequent co-authors include Tomasz Malisiewicz, Richard Newcombe, Tsun-Yi Yang, Julian Straub, Paul-Edouard Sarlin, Chris Sweeney, Peter Kontschieder, Samuel Rota Bulò, Ian Reid and Vassileios Balntas. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
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.