Benjamı́n Béjar

1.3k citations
34 papers · 651 indexed · h-index 12

Impact in

Papers in

Benjamı́n Béjar

31 papers receiving 637 citations

Peers

Benjamı́n Béjar
Comparison fields: 5 of 89
  • Health Informatics 24
  • Computer Vision and Pattern Recognition 165
  • Surgery 251
  • Human-Computer Interaction 29
  • Biomedical Engineering 199
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Citations per year

Countries citing papers authored by Benjamı́n Béjar

Since Specialization
Citations

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

Fields of papers citing papers by Benjamı́n Béjar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Benjamı́n Béjar. 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 Benjamı́n Béjar. The network helps show where Benjamı́n Béjar may publish in the future.

Co-authors

The 25 scholars most cited alongside Benjamı́n Béjar, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Benjamı́n Béjar Line = papers co-authored together Benjamı́n Béjar links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 34 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2017182
2 201398
3 201276
4 201347
5 201244
6 201329
7 201122
8 201019
9 201716
10 201414
11 201814
12 201511
13 20229
14
Improved Animal Tracking Algorithms Using Distributed Kalman-based Filters
20118
15 20127
16 20187
17 20247
18 20106
19 20115
20
Neural Message Passing on Hybrid Spatio-Temporal Visual and Symbolic Graphs for Video Understanding
20194

About Benjamı́n Béjar

Benjamı́n Béjar is a scholar working on Electrical and Electronic Engineering, Computer Networks and Communications, Computer Vision and Pattern Recognition, Computational Mechanics and Artificial Intelligence, having authored 34 papers that have together received 651 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (7 papers), Distributed Control Multi-Agent Systems (5 papers), Human Pose and Action Recognition (5 papers), Indoor and Outdoor Localization Technologies (5 papers), Cooperative Communication and Network Coding (4 papers), Energy Efficient Wireless Sensor Networks (4 papers), Target Tracking and Data Fusion in Sensor Networks (4 papers) and Distributed Sensor Networks and Detection Algorithms (3 papers). The work is most often cited by research in Health Informatics (24 citations), Computer Vision and Pattern Recognition (165 citations), Surgery (251 citations), Human-Computer Interaction (29 citations) and Biomedical Engineering (199 citations). Benjamı́n Béjar has collaborated with scholars based in Switzerland, Spain and United States. Frequent co-authors include Renè Vidal, Luca Zappella, Santiago Zazo, Gregory D. Hager, Andrea M. Tonello, Fabio Versolatto, Yixin Gao, Martin Vetterli, Narges Ahmidi and Sanjeev Khudanpur. Their work appears in journals such as IEEE Transactions on Signal Processing, Proceedings of the National Academy of Sciences, Geoscientific model development, Applied Energy and Journal of Applied Crystallography.

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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