Staff profile

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)

• Deep Learning
• Image Recognition
• Information Fusion

Conference Paper

• Dream-Box: Object-wise Outlier Generation for Out-of-Distribution Detection
  
  Isaac-Medina, B., & Breckon, T. (in press). Dream-Box: Object-wise Outlier Generation for Out-of-Distribution Detection. In Proc. Computer Vision Pattern Recognition Workshops. IEEE.
• Semi-supervised Object-Wise Anomaly Detection for Firearm and Firearm Component Detection in X-ray Security Imagery
  
  Gaus, Y. F. A., Isaac-Medina, B. K. S., Bhowmik, N., Lam, Y. T., & Breckon, T. P. (in press). Semi-supervised Object-Wise Anomaly Detection for Firearm and Firearm Component Detection in X-ray Security Imagery. Presented at 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Nashville, Tennessee, USA.
• Performance Evaluation of Segment Anything Model with Variational Prompting for Application to Non-Visible Spectrum Imagery
  
  Gaus, Y. F. A., Bhowmik, N., Isaac-Medina, B. K. S., & Breckon, T. P. (2024). Performance Evaluation of Segment Anything Model with Variational Prompting for Application to Non-Visible Spectrum Imagery. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (pp. 3142-3152). IEEE. https://doi.org/10.1109/CVPRW63382.2024.00320
• Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields
  
  Isaac-Medina, B., Willcocks, C., & Breckon, T. (2023, June). Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields. Presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023, Vancouver, BC.
• Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery
  
  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). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CVPRW59228.2023.00301
• Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening
  
  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). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CVPRW59228.2023.00059
• Cross-modal Image Synthesis in Dual-Energy X-Ray Security Imagery
  
  Isaac-Medina, B., Bhowmik, N., Willcocks, C., & Breckon, T. (2022, June). Cross-modal Image Synthesis in Dual-Energy X-Ray Security Imagery. Presented at 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), New Orleans, Louisiana. https://doi.org/10.1109/cvprw56347.2022.00048
• UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-Identification in Video Imagery
  
  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. Presented at 2022 17th International Conference on Computer Vision Theory and Applications. https://doi.org/10.5220/0010836600003124
• Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark
  
  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. Presented at 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, BC, Canada. https://doi.org/10.1109/iccvw54120.2021.00142
• Visible to Infrared Transfer Learning as a Paradigm for Accessible Real-time Object Detection and Classification in Infrared Imagery
  
  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.. SPIE. https://doi.org/10.1117/12.2573968