M.C. Nechyba

1.3k citations
41 papers · 883 indexed · h-index 16

M.C. Nechyba

39 papers receiving 804 citations

Peers

M.C. Nechyba
Comparison fields: 5 of 79
  • Computer Vision and Pattern Recognition 357
  • Aerospace Engineering 353
  • Automotive Engineering 133
  • Control and Systems Engineering 226
  • Human-Computer Interaction 31
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W. Whittaker United States
Ivan Marković Croatia
Christoph Sprunk Germany
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Citations per year

Countries citing papers authored by M.C. Nechyba

Since Specialization
Citations

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

Fields of papers citing papers by M.C. Nechyba

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by M.C. Nechyba. 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 M.C. Nechyba. The network helps show where M.C. Nechyba may publish in the future.

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20220
2 200521
3 20052
4 200413
5 20049
6 20030
7 2003141
8
Autonomous agent navigation based on textural analysis
20037
9 2003108
10 200217
11
Flexible Wing Base Micro Aerial Vehicles: Vision-Guided Flight Stability and Autonomy for Micro Air Vehicles
20023
12 200237
13
Flexible Wing Base Micro Aerial Vehicles: Towards Flight Autonomy: Vision-Based Horizon Detection for Micro Air Vehicles
20025
14 200220
15
The Next Generation Autonomous Lawn Mower
20004
16
Learning to Fly: Modeling Human Control Strategies in an Aerial Vehicle
20003
17
Learning to Fly: Design and Construction of an Autonomous Airplane
19991
18 199714
19 199782
20 199528

About M.C. Nechyba

M.C. Nechyba is a scholar working on Automotive Engineering, Computer Vision and Pattern Recognition, Social Psychology, Aerospace Engineering and Control and Systems Engineering, having authored 41 papers that have together received 883 indexed citations. Recurring topics across this work include Autonomous Vehicle Technology and Safety (13 papers), Human-Automation Interaction and Safety (10 papers), Advanced Image and Video Retrieval Techniques (7 papers), Robotic Path Planning Algorithms (6 papers), Neural Networks and Applications (6 papers), Robotics and Sensor-Based Localization (6 papers), Traffic Prediction and Management Techniques (5 papers) and Advanced Vision and Imaging (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (357 citations), Aerospace Engineering (353 citations), Automotive Engineering (133 citations), Control and Systems Engineering (226 citations) and Human-Computer Interaction (31 citations). M.C. Nechyba has collaborated with scholars based in United States, Hong Kong and Sweden. Frequent co-authors include Yangsheng Xu, Peter Ifju, Yangsheng Xu, Scott Ettinger, Martin R. Waszak, Siniša Todorović, Yangsheng Xu, Kan Deng, Andrew Moore and Yeung Yam. Their work appears in journals such as IEEE Control Systems, Robotics and Autonomous Systems, IEEE Transactions on Robotics and Automation, Advanced Robotics and IEEE Transactions on Pattern Analysis and Machine Intelligence.

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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