Chris McCool
Impact in
- Signal Processing top 1%
- Biometric Identification and Security
- Analytical Chemistry top 0.5%
- Spectroscopy and Chemometric Analyses
Papers in
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- Enhanced Oil Recovery Techniques 27
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- Face and Expression Recognition 12
- Face recognition and analysis 11
- Co-authors
- Tristán Pérez (17 shared papers)Ben Upcroft (9 shared papers)Feras Dayoub (9 shared papers)Inkyu Sa (7 shared papers)Zongyuan Ge (8 shared papers)Chris Lehnert (12 shared papers)Sébastien Marcel (19 shared papers)G. Paul Willhite (31 shared papers)
- Journals
- IEEE Robotics and Automation Letters (10 papers)SPE Journal (7 papers)Journal of Field Robotics (3 papers)SPE Reservoir Evaluation & Engineering (2 papers)IEEE Pervasive Computing (1 paper)
- Partner nations
- AustraliaUnited StatesGermany
In The Last Decade
Chris McCool
112 papers receiving 4.0k citations
Chris McCool's Hit Papers
Peers
Comparison fields: 5 of 142
- Signal Processing 648
- Analytical Chemistry 536
- Plant Science 2.0k
- Computer Vision and Pattern Recognition 1.1k
- Ocean Engineering 452
Countries citing papers authored by Chris McCool
This map shows the geographic impact of Chris McCool'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 Chris McCool with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chris McCool more than expected).
Fields of papers citing papers by Chris McCool
This network shows the impact of papers produced by Chris McCool. 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 Chris McCool. The network helps show where Chris McCool may publish in the future.
Co-authors
The 25 scholars most cited alongside Chris McCool, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 113 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | DeepFruits: A Fruit Detection System Using Deep Neural Networks Hit paper breakdown → | 2016 | 899 |
| 2 | 2017 | 199 | |
| 3 | 2012 | 184 | |
| 4 | 2017 | 162 | |
| 5 | 2017 | 148 | |
| 6 | 2012 | 134 | |
| 7 | 2015 | 107 | |
| 8 | 2018 | 105 | |
| 9 | 2017 | 103 | |
| 10 | 2020 | 86 | |
| 11 | 2009 | 85 | |
| 12 | 2020 | 74 | |
| 13 | 2016 | 74 | |
| 14 | 2016 | 67 | |
| 15 | 2017 | 63 | |
| 16 | 2004 | 62 | |
| 17 | 2018 | 60 | |
| 18 | 2014 | 60 | |
| 19 | 1998 | 59 | |
| 20 | 2013 | 58 |
About Chris McCool
Chris McCool is a scholar working on Ocean Engineering, Computer Vision and Pattern Recognition, Signal Processing, Plant Science and Mechanical Engineering, having authored 113 papers that have together received 4.2k indexed citations. Recurring topics across this work include Smart Agriculture and AI (34 papers), Enhanced Oil Recovery Techniques (27 papers), Hydraulic Fracturing and Reservoir Analysis (15 papers), Hydrocarbon exploration and reservoir analysis (14 papers), Robotics and Sensor-Based Localization (13 papers), Face and Expression Recognition (12 papers), Face recognition and analysis (11 papers) and Biometric Identification and Security (10 papers). The work is most often cited by research in Signal Processing (648 citations), Analytical Chemistry (536 citations), Plant Science (2.0k citations), Computer Vision and Pattern Recognition (1.1k citations) and Ocean Engineering (452 citations). Chris McCool has collaborated with scholars based in Australia, United States and Germany. Frequent co-authors include Tristán Pérez, Ben Upcroft, Feras Dayoub, Inkyu Sa, Zongyuan Ge, Chris Lehnert, Sébastien Marcel, G. Paul Willhite, D. W. Green and Andrew R. English. Their work appears in journals such as IEEE Robotics and Automation Letters, SPE Journal, Journal of Field Robotics, SPE Reservoir Evaluation & Engineering and IEEE Pervasive Computing.
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.