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News and Resources for Members of the IEEE Signal Processing Society
Title: Recent Advances of Deep Learning within X-ray Security Imaging
Date: 24 February 2022
Time: 11:00 AM EST (New York time)
Duration: Approximately 1 Hour
Presenters: Dr. Samet Akcay
Based on the IEEE Xplore® article: Using Deep Convolutional Neural Network Architectures for Object Classification and Detection Within X-Ray Baggage Security Imagery
Published: IEEE Transactions on Information Forensics and Security, March 2018
Download: Original article will be made freely available for download for 48 hours from the day of the webinar, on IEEE Xplore®
X-ray security screening is widely utilized in aviation and transportation, and its importance has sparked interest in automated screening systems. The goal of this webinar is to explore computerized X-ray security imaging methods by classifying them into traditional machine learning and modern deep learning applications. The talk briefly reviews the traditional machine learning methodologies used in X-ray security imaging, and subsequently delves deeper into the applications of recent deep learning-based algorithms. The suggested taxonomy divides deep learning applications into supervised and unsupervised learning categories, with a focus on object categorization, detection, segmentation, and anomaly detection. The session goes on to look at some well-known X-ray datasets and presents a performance benchmark. The talk will be concluded with a discussion and future directions for X-ray security imagery, based on current and future advances in deep learning.
Dr. Samet Akcay received his PhD degree in the Department of Computer Science at Durham University, UK. Prior to pursuing a PhD, he received his MSc degree from the Robust Machine Intelligence Lab at the Department of Electrical Engineering at Penn State University, USA.
He is currently an AI Research Engineer/Scientist at Intel. He is working on self-supervised anomaly detection and localization for industrial and medical applications and recently open-sourced one of the largest anomaly detection libraries in the field. His primary research interests are real-time image classification, detection, anomaly detection, and unsupervised feature learning via deep/machine learning algorithms.
Dr. Akcay has published numerous academic papers in the leading computer vision and machine/deep learning conferences and journals and contributed to several projects funded by the UK Home Office, Department for Transport, and the Ministry of Defence.
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