Staff profile
Overview
https://apps.dur.ac.uk/biography/image/1731
| Affiliation | Telephone |
|---|---|
| Post Doctoral Research Associate in the Department of Computer Science |
Biography
Education
PhD - Durham University, UK (2019 - 2023)
MSc. Internet Systems and E-Business - Durham University, UK (2017-2018)
BEng (Mechatronics) - Universidad Autónoma de Yucatán, MX (2011 - 2017)
Research interests
- Deep Learning
- Image Recognition
- Information Fusion
Publications
Conference Paper
- Isaac-Medina, B., Willcocks, C., & Breckon, T. (2023). Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields.
- Gaus, Y., Bhowmik, N., Issac-Medina, B., Atapour-Abarghouei, A., Shum, H., & Breckon, T. (2023). Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). https://doi.org/10.1109/CVPRW59228.2023.00301
- Issac-Medina, B., Yucer, S., Bhowmik, N., & Breckon, T. (2023). Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). https://doi.org/10.1109/CVPRW59228.2023.00059
- Isaac-Medina, B., Bhowmik, N., Willcocks, C., & Breckon, T. (2022). Cross-modal Image Synthesis in Dual-Energy X-Ray Security Imagery. . https://doi.org/10.1109/cvprw56347.2022.00048
- Organisciak, D., Poyser, M., Alsehaim, A., Hu, S., Isaac-Medina, B. K., Breckon, T. P., & Shum, H. P. (2022). UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-Identification in Video Imagery. . https://doi.org/10.5220/0010836600003124
- Isaac-Medina, B. K., Poyser, M., Organisciak, D., Willcocks, C. G., Breckon, T. P., & Shum, H. P. (2021). Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark. . https://doi.org/10.1109/iccvw54120.2021.00142
- Gaus, Y., Bhowmik, N., Isaac-Medina, B., & Breckon, T. (2020). Visible to Infrared Transfer Learning as a Paradigm for Accessible Real-time Object Detection and Classification in Infrared Imagery. In H. Bouma, R. Prabhu, R. J. Stokes, & Y. Yitzhaky (Eds.), Proceedings volume 11542, counterterrorism, crime fighting, forensics, and surveillance technologies IV. https://doi.org/10.1117/12.2573968