Curtis Padgett

1.4k total citations
41 papers, 1.1k citations indexed

About

Curtis Padgett is a scholar working on Aerospace Engineering, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Curtis Padgett has authored 41 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Aerospace Engineering, 16 papers in Computer Vision and Pattern Recognition and 10 papers in Artificial Intelligence. Recurrent topics in Curtis Padgett's work include Robotics and Sensor-Based Localization (13 papers), Infrared Target Detection Methodologies (8 papers) and Robotic Path Planning Algorithms (6 papers). Curtis Padgett is often cited by papers focused on Robotics and Sensor-Based Localization (13 papers), Infrared Target Detection Methodologies (8 papers) and Robotic Path Planning Algorithms (6 papers). Curtis Padgett collaborates with scholars based in United States, Norway and Switzerland. Curtis Padgett's co-authors include Kenneth Kreutz-Delgado, Garrison W. Cottrell, Mohammad R. Jahanshahi, Sami F. Masri, Gaurav S. Sukhatme, Matthew N. Dailey, Ralph Adolphs, Yao Sui, Yuanwei Wu and Guanghui Wang and has published in prestigious journals such as Journal of Cognitive Neuroscience, Pattern Recognition and Neurocomputing.

In The Last Decade

Curtis Padgett

40 papers receiving 1.0k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Curtis Padgett United States 12 417 370 274 215 179 41 1.1k
Lauri Savioja Finland 26 146 0.4× 943 2.5× 364 1.3× 64 0.3× 63 0.4× 140 2.7k
Jianhui Zhao China 13 183 0.4× 566 1.5× 188 0.7× 29 0.1× 19 0.1× 52 999
Mohamed Daoudi France 28 139 0.3× 2.2k 5.9× 614 2.2× 25 0.1× 299 1.7× 125 2.7k
J.-Y. Bouguet United States 10 474 1.1× 1.4k 3.7× 67 0.2× 17 0.1× 58 0.3× 11 1.7k
Anbang Yao China 15 234 0.6× 1.2k 3.3× 44 0.2× 27 0.1× 203 1.1× 29 1.5k
Fabio Antonacci Italy 24 203 0.5× 570 1.5× 460 1.7× 139 0.6× 18 0.1× 183 2.3k
Thibaut Weise Switzerland 13 162 0.4× 1.3k 3.6× 537 2.0× 17 0.1× 83 0.5× 19 1.7k
David K. Han United States 22 126 0.3× 906 2.4× 39 0.1× 34 0.2× 57 0.3× 103 1.5k
Antoine Chaigne France 18 44 0.1× 714 1.9× 144 0.5× 142 0.7× 63 0.4× 45 1.4k
Zhenhua Chai China 14 73 0.2× 895 2.4× 81 0.3× 51 0.2× 111 0.6× 40 1.2k

Countries citing papers authored by Curtis Padgett

Since Specialization
Citations

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

Fields of papers citing papers by Curtis Padgett

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Curtis Padgett

This figure shows the co-authorship network connecting the top 25 collaborators of Curtis Padgett. A scholar is included among the top collaborators of Curtis Padgett 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 Curtis Padgett. Curtis Padgett 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
1.
Georgakis, Georgios, et al.. (2024). Pixel to Elevation: Learning to Predict Elevation Maps at Long Range Using Images for Autonomous Offroad Navigation. IEEE Robotics and Automation Letters. 9(7). 6170–6177. 4 indexed citations
2.
Atha, Deegan, Julian Nubert, David D. Fan, et al.. (2024). RoadRunner—Learning Traversability Estimation for Autonomous Off-Road Driving. 1. 192–212. 18 indexed citations
3.
Nissov, M., et al.. (2024). Robust High-Speed State Estimation for Off-Road Navigation Using Radar Velocity Factors. IEEE Robotics and Automation Letters. 9(12). 11146–11153. 3 indexed citations
4.
Hook, Joshua Vander, et al.. (2022). Topographical Landmarks for Ground-Level Terrain Relative Navigation on Mars. 2022 IEEE Aerospace Conference (AERO). 1–6. 7 indexed citations
5.
Ebadi, Kamak, et al.. (2022). Toward Autonomous Localization of Planetary Robotic Explorers by Relying on Semantic Mapping. 2022 IEEE Aerospace Conference (AERO). 1–10. 6 indexed citations
6.
Pham, Tu-Hoa, Shreyansh Daftry, Barry Ridge, et al.. (2021). Rover Relocalization for Mars Sample Return by Virtual Template Synthesis and Matching. Lirias (KU Leuven). 6 indexed citations
7.
Donnellan, Andrea, Marlon Pierce, Jun Wang, et al.. (2019). The Quakes Concept for Observing and Mitigating Natural Disasters. 5347–5350. 1 indexed citations
8.
Wu, Yuanwei, et al.. (2018). Real-Time Obstacle Detection and Tracking for Sense-and-Avoid Mechanism in UAVs. IEEE Transactions on Intelligent Vehicles. 3(2). 185–197. 62 indexed citations
9.
Jahanshahi, Mohammad R., et al.. (2017). Accurate and Robust Scene Reconstruction in the Presence of Misassociated Features for Aerial Sensing. Journal of Computing in Civil Engineering. 31(6). 6 indexed citations
10.
Dailey, Matthew N., Garrison W. Cottrell, & Curtis Padgett. (2006). A Mixture of Experts Model Exhibiting Prosopagnosia. eScholarship (California Digital Library). 1 indexed citations
11.
Liebe, Carl Christian, A. Abramovici, J. Chapsky, et al.. (2004). Laser radar for spacecraft guidance applications. 6. 6_2647–6_2662. 12 indexed citations
12.
Dailey, Matthew N., Garrison W. Cottrell, Curtis Padgett, & Ralph Adolphs. (2002). EMPATH: A Neural Network that Categorizes Facial Expressions. Journal of Cognitive Neuroscience. 14(8). 1158–1173. 179 indexed citations
13.
Padgett, Curtis, et al.. (2002). A hierarchical, automated target recognition algorithm for a parallel analog processor. 2. 374–379. 1 indexed citations
14.
Howard, Ayanna, Curtis Padgett, & Kenneth Brown. (2000). Real Time Intelligent Target Detection and Analysis with Machine Vision. SMARTech Repository (Georgia Institute of Technology). 6 indexed citations
15.
Padgett, Curtis, et al.. (2000). Identification using Bayesian decision theory. IEEE Transactions on Aerospace and Electronic Systems. 36(3). 773–783. 41 indexed citations
16.
Howard, Ayanna & Curtis Padgett. (1999). A generalized approach to real-time pattern recognition in sensed data. Pattern Recognition. 32(12). 2069–2071. 1 indexed citations
17.
Liebe, Carl Christian, Kenneth Brown, Curtis Padgett, et al.. (1998). <title>VIGIL: a GPS-based target-tracking system</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 3365. 10–21. 1 indexed citations
18.
Padgett, Curtis, et al.. (1997). <title>VIGILANTE: an advanced sensing/processing testbed for ATR applications</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 3069. 82–93. 4 indexed citations
19.
Padgett, Curtis & Garrison W. Cottrell. (1996). Representing Face Images for Emotion Classification. Neural Information Processing Systems. 9. 894–900. 126 indexed citations
20.
Padgett, Curtis & Garrison W. Cottrell. (1995). Identifying Emotion in Static Face Images. 19 indexed citations

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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