Ferenc Bálint-Benczédi

458 citations
21 papers · 246 indexed · h-index 10
Topics
Robotics and Sensor-Based Localization (12 papers)Advanced Image and Video Retrieval Techniques (8 papers)Multimodal Machine Learning Applications (5 papers)

In The Last Decade

Ferenc Bálint-Benczédi

20 papers receiving 236 citations

Peers

Ferenc Bálint-Benczédi
Comparison fields: 5 of 62
  • Computer Vision and Pattern Recognition 118
  • Control and Systems Engineering 103
  • Artificial Intelligence 103
  • Aerospace Engineering 55
  • Mechanical Engineering 19
Replace Yoichiro Endo with:
Yoichiro Endo United States
Dominik Jain Germany
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Gi Hyun Lim Portugal
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Beom-Su Seo South Korea
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Ferenc Bálint-Benczédi relative to Yoichiro Endo United States Yoichiro Endo's profile →
Citations per field
00.5×9.5×
Yoichiro Endo · 1×
Citations per year

Countries citing papers authored by Ferenc Bálint-Benczédi

Since Specialization
Citations

This map shows the geographic impact of Ferenc Bálint-Benczédi'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 Ferenc Bálint-Benczédi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ferenc Bálint-Benczédi more than expected).

Fields of papers citing papers by Ferenc Bálint-Benczédi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ferenc Bálint-Benczédi. 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 Ferenc Bálint-Benczédi. The network helps show where Ferenc Bálint-Benczédi may publish in the future.

Co-authorship network of co-authors of Ferenc Bálint-Benczédi

This figure shows the co-authorship network connecting the top 25 collaborators of Ferenc Bálint-Benczédi. A scholar is included among the top collaborators of Ferenc Bálint-Benczédi 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 Ferenc Bálint-Benczédi. Ferenc Bálint-Benczédi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 12
2 3
3 4
4 7
5 0
6 13
7 6
8 5
9 45
10 26
11 6
12 16
13 7
14 19
15 1
16 20
17 21
18
Ensembles of Strong Learners for Multi-cue Classification
3
19
Efficient Part-Graph Hashes for Object Categorization
1
20 18

About Ferenc Bálint-Benczédi

Ferenc Bálint-Benczédi is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Industrial and Manufacturing Engineering, having authored 21 papers that have together received 246 indexed citations. Recurring topics across this work include Robotics and Sensor-Based Localization (12 papers), Advanced Image and Video Retrieval Techniques (8 papers) and Multimodal Machine Learning Applications (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (118 citations), Control and Systems Engineering (103 citations) and Medical Laboratory Technology (5 citations). Ferenc Bálint-Benczédi has collaborated with scholars based in Germany, United Kingdom and Japan. Frequent co-authors include Michael Beetz, Zoltán-Csaba Márton, Daniel Nyga, Nico Blodow, Moritz Tenorth, Stefan Profanter, Dejan Pangercic, Alexis Maldonado, Karol Hausman and G. Bartels. Their work appears in journals such as Journal of Affective Disorders, Pattern Recognition Letters and Journal of Intelligent & Robotic Systems.

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.

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