Jon Crall, Ph.D.

Staff R&D Engineer

Computer Vision

Kitware New York
Clifton Park, NY

Ph.D. in Computer Vision
Rensselaer Polytechnic Institute

B.S. in Computer Science
State University of New York Polytechnic Institute

Jon Crall

Jon Crall, Ph.D., is a staff R&D engineer on Kitware’s Computer Vision (CV) Team located in Clifton Park, New York. He’s a machine learning and Python expert. He uses deep learning to solve computer vision problems in areas such as classification, detection, and segmentation.

Jon’s research has been funded by various agencies including the Air Force Research Laboratory (AFRL), National Geospatial-Intelligence Agency (NGA), Defense Advanced Research Projects Agency (DARPA), Intelligence Advanced Research Projects Activity (IARPA), and the National Oceanic and Atmospheric Administration (NOAA). He applies recent advances in computer vision to his government and environmental projects.

In addition to his research, Jon has made significant contributions to a number of successful proposals valued at $2M. His involvement includes proposal data fusion and temporally aware classification decisions. The most recent of these proposals is IARPA SMART.

Jon is also a member of Kitware’s internal consulting group for AI. The purpose of this group is to help increase AI use and understanding across the company.

Shortly after receiving his Ph.D. in 2017, Jon placed 10th in the international TopCoder Urban 3D Challenge that was sponsored by the United States Special Operations Command (USSOCOM) and NASA. In this challenge, he had to compete against 53 other contestants to use satellite imagery and 3D height data software to improve automated building detection and labeling. He was the only U.S. citizen to place in the top 20.

Prior to joining Kitware full-time, Jon interned with Kitware’s CV Team. He helped expand the unit-testing suite for CV projects and also worked as an annotator. In fact, Jon’s annotation work led to him making improvements to the software he was using.

While Jon was working on his Ph.D., he served as the lead image-analysis developer for the Wildbook project (formerly the Image Based Ecological Information System — IBEIS). His work on this project was adopted by multiple research teams around the world and led to six publications.

Jon received his Ph.D. in computer vision from Rensselaer Polytechnic Institute in 2017. His thesis focused on the problem of instance recognition of individual animals. In 2010, he received his bachelor’s degree in computer science with a minor in math from the State University of New York Polytechnic Institute.

Invited Talks & Media

  • “Developing With Doctests,” presented talk, Python Conference (PyCon), 2020


  1. A. Hoogs, M. Dawkins, B. Richards, G. Cutter, D. Hart, M. Clarke, W. Michaels, J. Crall, L. Sherrill, N. Siekierski, M. Woehlke, and K. Edwards, "An Open-Source System for Do-It-Yourself AI in the Marine Environment," in Ocean Sciences Meeting, 2020. [URL]
  2. C. Funk, J. Crall, W. Hicks, C. Law, P. Tunison, R. Blue, A. Hoogs, T. Rovito, and A. Maltenfort, "WEFT Feature Detection and Mensuration for Airplane Classification in Satellite Imagery," in MSS National Symposium on Sensor and Data Fusion, 2019.
  3. J. Crall, J. Becker, P. Tunison, M. Dawkins, A. Basharat, R. Blue, M. Turek, and A. Hoogs, "Deep Learning for Small Object Detection in Satellite Infrared Imagery," in Proceedings of the MSS National Symposium on Sensor and Data Fusion, 2018.
  4. J. Parham, C. Stewart, J. Crall, D. Rubenstein, J. Holmberg, and T. Berger-Wolf, "An animal detection pipeline for identification," in Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2018. [URL]
  5. J. Parham, C. Stewart, J. Crall, D. Rubenstein, J. Holmberg, and T. Berger-Wolf, "Focusing animal identification with annotations of interest," in Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2018.
  6. J. Parham, J. Crall, C. Stewart, T. Berger-Wolf, and D. Rubenstein, "Animal population censusing at scale with citizen science and photographic identification," in AAAI Spring Symposium Series, 2017. [URL]
  7. J. Crall, "Identifying Individual Animals using Ranking, Verification, and Connectivity," Ph.D. dissertation, Rensselaer Polytechnic Institute, 2017.
  8. S. Dunbar, D. Baumbach, M. Wright, C. Hayes, J. Holmberg, J. Crall, and C. Stewart, "HotSpotter: less manipulating, more learning, and better vision for turtle photo identification," in Annual Symposium on Sea Turtle Biology and Conservation, 2017.
  9. T. Morrison, D. Keinath, W. Estes-Zumpf, J. Crall, and C. Stewart, "Individual Identification of the Endangered Wyoming Toad Anaxyrus baxteri and Implications for Monitoring Species Recovery," Journal of Herpetology, vol. 50, no. 1, pp. 44-49, Mar. 2016. [URL]
  10. T. Berger-Wolf, J. Crall, J. Holberg, J. Parham, C. Stewart, B. Mackey, P. Kahumbu, and D. Rubenstein, "The Great Grevy’s Rally: The Need, Methods, Findings, Implications and Next Steps," Grevy's Zebra Trust, Aug. 2016. [URL]
  11. D. Rubenstein, C. Stewart, T. Berger-Wolf, J. Parham, J. Crall, C. Machogu, P. Kahumbu, and N. Maingi, "The Great Zebra and Giraffe Count: The Power and Rewards of Citizen Science," Kenya Wildlife Service, Jul. 2015. [URL]
  12. J. Crall, C. Stewart, T. Berger-Wolf, D. Rubenstein, and S. Sundaresan, "HotSpotter–Patterned species instance recognition," in Proceedings of the IEEE Workshop on Applications of Computer Vision, 2013. [URL]

Bibliography generated 2022-12-06-04:03:41 (5604)