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Welcome, my name is Jonathan Adams.
I manage a team of student-researchers in the FSU computer vision lab, located in the WIlliam-Johnston Building. The lab develops or assists with computer vision research projects, such as developing tools and techniques to create synthetic data sets. The synthetic data is then mixed with a much smaller number of authentic images to train computer vision models.
The lab includes some of the best and brightest students who actively conduct computer vision research. Currently, a team of five students work in the lab. In May of 2021 the first graduate of the lab started a company and has been inducted into the NVIDIA inception accelerator program!
The FSU computer vision lab focuses on working with YOLO and OpenCV projects as well as generating datasets that can be used for training AI systems. Our approach focuses on producing explainable algorithms that are capable of being put to work in the field.
The lab started with a research project that examined algorithms for search and rescue. We soon realized that quality images are essential to efficient training, and in many cases, there just are not enough of that kind of imagery in existence to train computer vision models. Our work hopes to provide synthetic datasets for all manner of AI work and research. Shout out to Dean Larry Dennis for supporting the effort to develop the lab.
Learn more at the FSU ML-Computer vision lab (link coming).
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Research
Padilla-Rodríguez, B.C., Adams, J. (2023) Acceptance of Online Degrees by Undergraduate Mexican Students: Comparing perceptions a decade later. Submitted to The Association for the Advancement of Computing in Education (AACE), Vienna, Austria.
Adams, J.L., Ravuri, B., Moja, O., Obermaier, L., Roberts, A. (2023) Uses of Artificial Intelligence in Higher Education. Submitted to The Association for the Advancement of Computing in Education (AACE), Vienna, Austria.
Sutor J., Adams, J. L. (2022) Exploring Synthetic Visual Data for Training Deep-Learning Based Classifiers.
Adams, J. L.,John Sutor, Ava Dodd and Erin Murph. (2021) Evaluating the Performance of Synthetic Visual Data for Real-Time Object Detection. The 6th International Conference on Communication, Image and Signal Processing (CCISP 2021) which will be held in Chengdu, China during Nov. 19-21, 2021.
Dodd, A., & Adams, J. L. (2021) The Role of Synthetic Data in Aerial Object Detection. Paper to be presented at International Marine, Aviation, Transport, Logistics and Trade, CMATLT001 2021: XV. In, to be held in Amsterdam, Netherlands. (Best Paper).
Adams, J. L., Erin Murphy, John Sutor, & Ava Dodd. (2021). Assessing the Quality and Production of Synthetic Visual Data. In 9th International Conference on Information and Education Technology (ICIET 2021), Okayama, Japan, March 27-29, 2021 (5 pages). co-sponsored by IEEE, Okayama University (Japan), South China Normal University (China), and International Academy of Computing Technology (Hong Kong).
Adams, J. L., & Mitchell, A. L. (2020). TESA: A pedagogical approach to engage, study, and activate technology learning in an interdisciplinary setting. In Association for the Advancement of Computing in Education (AACE) (Ed.), Proceedings of EdMedia + Innovate Learning The Netherlands (pp. 778-781). Waynesville, NC: Association for the Advancement of Computing in Education (AACE). Retrieved from https://www.learntechlib.org/primary/p/217389/.
Adams, J. L., Ava Dodd, John Sutor, & Erin Murphy. (2020). AI and Undergraduate Research: A Dialog in Project-Based Learning. In Gary H. Marks, & Denise Schmidt-Crawford (Eds.), Society for Information Technology & Teacher Education International Conference, Apr 07, 2020 in Online ISBN 978-1-939797-48-3. Association for the Advancement of Computing in Education (AACE), Chesapeake, VA. Retrieved from http://www.learntechlib.org/fromc/56493
Products submitted for commercialization
Shark-Finder (2022)
Binaural Beat Box (2022)
OSRAI (Ocean Search and Rescue Artificial Intelligence) (2023)