EU

Meet the Team

Connor Anderson

Senior R&D Engineer

Computer Vision

Kitware New York
Clifton Park, NY

Ph.D. in Computer Science
Brigham Young University

M.S. in Computer Science
Brigham Young University

B.S. in Computer Engineering
Brigham Young University

Connor Anderson

Connor is a Senior R&D Engineer working on the Computer Vision Team at KHQ. While at Kitware, he has worked on integrating cutting edge deep learning models and algorithms into image annotation workflows, including methods for finding and ranking novel visual samples in object detection datasets and semantic image search using text. He has also worked on automated detection and tracking in multiple domains.

Connor earned his Ph.D. in Computer Science from Brigham Young University (BYU). His primary research focus was in generating abstract synthetic images to train computer vision models to perform in real-world situations. He developed a new, efficient approach for generating large sets of random IFS fractals with the necessary complex structure for training visual recognition models, and explored the potential of masked image modeling and synthetic data for learning generalizable, cross-domain features during an internship and collaboration with IBM and MIT. He also worked on problems in fine-grained recognition and animal re-identification. He earned his M.S. in Computer Science, and B.S. in Computer Engineering, also from BYU.

Publications

  1. C. Anderson, E. Schenck, C. Reinhardt, R. Blue, and B. Clipp, "AA-Pipe: automatic annotation pipeline for visible and thermal infrared video," Optical Engineering, vol. 64, no. 09, Jun. 2025. [URL]
  2. C. Reinhardt, C. Anderson, and E. Schenck, "V2IR-CnLDM: a generative visible-to-infrared image translation using ControlNet-guided conditional latent diffusion model," Optical Engineering, vol. 64, no. 09, Jun. 2025. [URL]
  3. C. Anderson, M. Gwilliam, E. Gaskin, and R. Farrell, "Elusive Images: Beyond Coarse Analysis for Fine-Grained Recognition," in 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024. [URL]
  4. C. Anderson, "Procedural Pre-Training for Visual Recognition," Thesis, Brigham Young University, 2024.
  5. M. Gwilliam, A. Teuscher, C. Anderson, and R. Farrell, "Fair Comparison: Quantifying Variance in Results for Fine-grained Visual Categorization," in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), 2021. [URL]
  6. C. Anderson, A. Teuscher, E. Anderson, A. Larsen, J. Shirley, and R. Farrell, "Have Fun Storming the Castle(s)!," in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), 2021. [URL]
  7. C. Anderson and R. Farrell, "Improving Fractal Pre-training," 2021. [URL]
  8. A. Shukla, C. Anderson, G. Sigh Cheema, P. Gao, S. Onda, D. Anshumaan, S. Anand, and R. Farrell, "A Hybrid Approach to Tiger Re-Identification," in 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019. [URL]
  9. P. Guo, C. Anderson, K. Pearson, and R. Farrell, "Neural network interpretation via fine grained textual summarization," 2018.

Bibliography generated 2025-09-25-14:00:06 (8133)