Dave Barnes

438 citations
38 papers · 306 · h-index 11

Impact in

Papers in

Dave Barnes

33 papers receiving 288 citations

Peers

Dave Barnes
Comparison fields: 5 of 73
  • Computer Vision and Pattern Recognition 104
  • Astronomy and Astrophysics 73
  • Artificial Intelligence 96
  • Industrial and Manufacturing Engineering 24
  • Aerospace Engineering 54
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Benjamin Cichy United States
S. Natarajan India
Zhenzhen Wang China
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Citations per year

Countries citing papers authored by Dave Barnes

Since Specialization
Citations

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

Fields of papers citing papers by Dave Barnes

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Dave Barnes, 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 Dave Barnes Line = papers co-authored together Dave Barnes links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 38 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201245
2 200933
3 200928
4 201223
5 201223
6 198918
7 201116
8 200414
9 199712
10 201011
11 199910
12
Multi-Spectral Vision Processing for the ExoMars 2018 Mission
201110
13
Declarative sensor knowledge in a robot monitoring system
19878
14 20037
15 20097
16 20085
17
Determination of Planetary Meteorology from Aerobot Flight Sensors
20025
18
The PanCam Calibration Target (PCT) and multispectral image processing for the ExoMars 2018 mission
20114
19
Merged Shape from Shading and Shape from Stereo for Planetary Topographic Mapping
20144
20 20094

About Dave Barnes

Dave Barnes is a scholar working on Aerospace Engineering, Astronomy and Astrophysics, Computer Vision and Pattern Recognition, Artificial Intelligence and Mechanical Engineering, having authored 38 papers that have together received 306 indexed citations. Recurring topics across this work include Planetary Science and Exploration (11 papers), Astro and Planetary Science (6 papers), AI-based Problem Solving and Planning (6 papers), Modular Robots and Swarm Intelligence (5 papers), Image Retrieval and Classification Techniques (5 papers), Robotic Path Planning Algorithms (4 papers), Distributed systems and fault tolerance (4 papers) and Spacecraft Design and Technology (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (104 citations), Astronomy and Astrophysics (73 citations), Artificial Intelligence (96 citations), Industrial and Manufacturing Engineering (24 citations) and Aerospace Engineering (54 citations). Dave Barnes has collaborated with scholars based in United Kingdom, Germany and Austria. Frequent co-authors include Changjing Shang, Honghai Liu, George M. Coghill, Nigel Hardy, Qiang Shen, Andy Shaw, Andrew Shaw, D. Pullan, Derek Long and A. J. Coates. Their work appears in journals such as Robotica, Journal of Field Robotics, American Journal of Respiratory and Critical Care Medicine, Computer Vision and Image Understanding and International Journal of Approximate Reasoning.

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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