Dmytro Mishkin
- Computer Vision and Pattern Recognition top 5%
- Aerospace Engineering top 10%
- Artificial Intelligence
- Geology top 10%
- Media Technology top 10%
- Co-authors
- Jiřı́ MatasMichal PerďochFilip RadenovićFabio BellaviaMilan ŠulcDániel BaráthOndřej ChumWolfgang Förstner
- Topics
- Advanced Image and Video Retrieval Techniques (7 papers)Robotics and Sensor-Based Localization (5 papers)Advanced Vision and Imaging (3 papers)
- Journals
- Pattern Recognition LettersImage and Vision ComputingComputer Vision and Image Understanding
- Partner nations
- CzechiaSwitzerlandItaly
In The Last Decade
Dmytro Mishkin
8 papers receiving 399 citations
Peers
Comparison fields: 5 of 94
- Computer Vision and Pattern Recognition 277
- Aerospace Engineering 159
- Artificial Intelligence 77
- Geology 29
- Media Technology 27
Countries citing papers authored by Dmytro Mishkin
This map shows the geographic impact of Dmytro Mishkin'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 Dmytro Mishkin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dmytro Mishkin more than expected).
Fields of papers citing papers by Dmytro Mishkin
This network shows the impact of papers produced by Dmytro Mishkin. 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 Dmytro Mishkin. The network helps show where Dmytro Mishkin may publish in the future.
Co-authorship network of co-authors of Dmytro Mishkin
This figure shows the co-authorship network connecting the top 25 collaborators of Dmytro Mishkin. A scholar is included among the top collaborators of Dmytro Mishkin 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 Dmytro Mishkin. Dmytro Mishkin 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 | 6 | |
| 3 | 18 | |
| 4 | 3 | |
| 5 | Working hard to know your neighbor's margins: Local descriptor learning loss | 100 |
| 6 | 188 | |
| 7 | Learning Discriminative Affine Regions via Discriminability. | 4 |
| 8 | Very Deep Residual Networks with MaxOut for Plant Identification in the Wild. | 9 |
| 9 | 93 |
About Dmytro Mishkin
Dmytro Mishkin is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Media Technology, having authored 9 papers that have together received 421 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (7 papers), Robotics and Sensor-Based Localization (5 papers) and Advanced Vision and Imaging (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (277 citations), Aerospace Engineering (159 citations) and Geology (29 citations). Dmytro Mishkin has collaborated with scholars based in Czechia, Switzerland and Italy. Frequent co-authors include Jiřı́ Matas, Michal Perďoch, Filip Radenović, Fabio Bellavia, Milan Šulc, Dániel Baráth, Ondřej Chum and Wolfgang Förstner. Their work appears in journals such as Pattern Recognition Letters, Image and Vision Computing and Computer Vision and Image Understanding.
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.