Staff profile

Neelanjan Bhowmik is a Post-doc research associate at the Department of Computer Science at Durham University. Before joining Durham in 2018, he was a Researcher at Institut Géographique National (Paris, France). He received PhD in computer vision/image processing from Université Paris-Est and Institut Géographique National (Paris, France) in 2017. Prior to that, He received MSc degree in Computer Vision Engineering from the University of Sheffield, UK in 2012 and Bachelor of Engineering degree in Electronics Engineering from RTM Nagpur University, India in 2008. From 2008 to 2011, he worked as a Technical Associate at Tech Mahindra Limited, India.

His primary research interests, in the domain of applied computer vision & image processing, are as follows: object detection, classification, automated X-ray imagery screening/analysis, supervised/unsupervised learning via deep/machine learning, content-based image retrieval, image localization.

Research Group

Department of Computer Science

• Vision, Imaging and Visualisation in Durham (VIViD)

Research Interests

• Object detection/classification
• 2D / 3D CT X-ray Imaging
• Content-based image retrieval/recognition
• Deep learning
• Machine learning
• Computer vision
• Image processing

Conference Paper

• Isaac-Medina, B., Gaus, Y., Bhowmik, N., & Breckon, T. (2024, September). Towards Open-World Object-based Anomaly Detection via Self-Supervised Outlier Synthesis. Paper presented at European Conference on Computer Vision, Milan, Italy
• Gaus, Y. F. A., Bhowmik, N., Isaac-Medina, B. K. S., & Breckon, T. P. (2024, June). Performance Evaluation of Segment Anything Model with Variational Prompting for Application to Non-Visible Spectrum Imagery. Presented at 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA
• Gaus, Y., Bhowmik, N., Issac-Medina, B., Atapour-Abarghouei, A., Shum, H., & Breckon, T. (2023, June). Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery. Presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023, Vancouver, BC
• Barker, J., Bhowmik, N., Gaus, Y., & Breckon, T. (2023, February). Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption. Presented at VISAPP 2023: 18th International Conference on Computer Vision Theory and Applications, Lisbon, Portugal
• Issac-Medina, B., Yucer, S., Bhowmik, N., & Breckon, T. (2023, June). Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening. Presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023, Vancouver, BC
• Corona-Figueroa, A., Bond-Taylor, S., Bhowmik, N., Gaus, Y. F. A., Breckon, T. P., Shum, H. P., & Willcocks, C. G. (2023, October). Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers. Presented at ICCV23: 2023 IEEE/CVF International Conference on Computer Vision, Paris, France
• Bhowmik, N., & Breckon, T. (2022, December). Joint Sub-component Level Segmentation and Classification for Anomaly Detection within Dual-Energy X-Ray Security Imagery. Presented at International Conference on Machine Learning Applications, Bahamas
• Bhowmik, N., Barker, J., Gaus, Y., & Breckon, T. (2022, June). Lost in Compression: the Impact of Lossy Image Compression on Variable Size Object Detection within Infrared Imagery. Presented at 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), New Orleans, Louisiana
• 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
• Bhowmik, N., Gaus, Y., & Breckon, T. (2021, September). On the Impact of Using X-Ray Energy Response Imagery for Object Detection via Convolutional Neural Networks. Presented at International Conference on Image Processing, Anchorage, AK
• Thomson, W., Bhowmik, N., & Breckon, T. (2020, December). Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection. Presented at 19th IEEE International Conference on Machine Learning and Applications (ICMLA 2020), Miami, FL
• Wang, Q., Bhowmik, N., & Breckon, T. (2020, December). Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery. Presented at 19th IEEE International Conference on Machine Learning and Applications (ICMLA 2020), Miami, Florida
• Gaus, Y., Bhowmik, N., Isaac-Medina, B., & Breckon, T. (2020, September). Visible to Infrared Transfer Learning as a Paradigm for Accessible Real-time Object Detection and Classification in Infrared Imagery. Presented at Spie Security + Defence