Pavel Moravec
- Artificial Intelligence
- Computational Theory and Mathematics top 10%
- Information Systems
- Computer Vision and Pattern Recognition
- Signal Processing
- Co-authors
- Václav SnåšelJaroslav PokornýIvana ČernáPetr GajdošJiřı ŠimšaLuboš BrimJan PlatošRadek Pelánek
- Topics
- Image Retrieval and Classification Techniques (7 papers)Formal Methods in Verification (7 papers)Semantic Web and Ontologies (6 papers)
In The Last Decade
Pavel Moravec
26 papers receiving 150 citations
Peers
Comparison fields: 5 of 39
- Artificial Intelligence 80
- Computational Theory and Mathematics 44
- Information Systems 43
- Computer Vision and Pattern Recognition 33
- Signal Processing 32
Countries citing papers authored by Pavel Moravec
This map shows the geographic impact of Pavel Moravec'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 Pavel Moravec with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pavel Moravec more than expected).
Fields of papers citing papers by Pavel Moravec
This network shows the impact of papers produced by Pavel Moravec. 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 Pavel Moravec. The network helps show where Pavel Moravec may publish in the future.
Co-authorship network of co-authors of Pavel Moravec
This figure shows the co-authorship network connecting the top 25 collaborators of Pavel Moravec. A scholar is included among the top collaborators of Pavel Moravec 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 Pavel Moravec. Pavel Moravec is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | Two-step Modified SOM for Parallel Calculation. | 1 |
| 3 | 6 | |
| 4 | Dimension Reduction Methods for Iris Recognition | 2 |
| 5 | 5 | |
| 6 | 1 | |
| 7 | 3 | |
| 8 | 4 | |
| 9 | Relaxed Cycle Condition Improves Partial Order Reduction | 1 |
| 10 | 3 | |
| 11 | Using BFA with WordNet Based Model for Web Retrieval. | 1 |
| 12 | Testing dimension reduction methods for image retrieval | 1 |
| 13 | 8 | |
| 14 | Testing Dimension Reduction Methods for Text Retrieval | 3 |
| 15 | Using BFA with WordNet Ontology Based Model for Web Retrieval | 2 |
| 16 | 23 | |
| 17 | Concept Lattice Generation by Singular Value Decomposition | 12 |
| 18 | WordNet Ontology Based Model for Information Retrieval. | 2 |
| 19 | LSI vs. Wordnet Ontology in Dimension Reduction for Information Retrieval | 10 |
| 20 | Vector Query with Signature Filtering | 1 |
About Pavel Moravec
Pavel Moravec is a scholar working on Software, Signal Processing and Artificial Intelligence, having authored 27 papers that have together received 158 indexed citations. Recurring topics across this work include Image Retrieval and Classification Techniques (7 papers), Formal Methods in Verification (7 papers) and Semantic Web and Ontologies (6 papers). The work is most often cited by research in Software (20 citations), Instrumentation (12 citations) and Signal Processing (32 citations). Pavel Moravec has collaborated with scholars based in Czechia, Poland and Russia. Frequent co-authors include Václav Snåšel, Jaroslav Pokorný, Ivana Černá, Petr Gajdoš, Jiřı Šimša, Luboš Brim, Jan Platoš, Radek Pelánek, Dušan Húsek and Jan Šmíd. Their work appears in journals such as Journal of Sensors, IFAC-PapersOnLine and Electronic Notes in Theoretical Computer Science.
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.