25 citations · 62 across the 17 of their papers we have counts for
19 papers · 1 filter
Average Calibration Losses for Reliable Uncertainty in Medical Image Segmentation
Theodore Barfoot, Luis C. Garcia-Peraza-Herrera, Samet Akcay +2
Deep neural networks for medical image segmentation are often overconfident, compromising both reliability and clinical utility. In this work, we propose differentiable formulation…
FEVER-OOD: Free Energy Vulnerability Elimination for Robust Out-of-Distribution Detection
Brian K. S. Isaac-Medina, Mauricio Che, Yona F. A. Gaus +2
Modern machine learning models, that excel on computer vision tasks such as classification and object detection, are often overconfident in their predictions for Out-of-Distributio…
Anomalib: A Deep Learning Library for Anomaly Detection
Samet Akcay, Dick Ameln, Ashwin Vaidya +3
This paper introduces anomalib, a novel library for unsupervised anomaly detection and localization. With reproducibility and modularity in mind, this open-source library provides…
A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband Items
Taimur Hassan, Samet Akcay, Mohammed Bennamoun +2
Screening cluttered and occluded contraband items from baggage X-ray scans is a cumbersome task even for the expert security staff. This paper presents a novel strategy that extend…
Tensor Pooling Driven Instance Segmentation Framework for Baggage Threat Recognition
Taimur Hassan, Samet Akcay, Mohammed Bennamoun +2
Automated systems designed for screening contraband items from the X-ray imagery are still facing difficulties with high clutter, concealment, and extreme occlusion. In this paper,…
Unsupervised Anomaly Instance Segmentation for Baggage Threat Recognition
Taimur Hassan, Samet Akcay, Mohammed Bennamoun +2
Identifying potential threats concealed within the baggage is of prime concern for the security staff. Many researchers have developed frameworks that can detect baggage threats fr…
Multi-Model Learning for Real-Time Automotive Semantic Foggy Scene Understanding via Domain Adaptation
Naif Alshammari, Samet Akcay, Toby P. Breckon
Robust semantic scene segmentation for automotive applications is a challenging problem in two key aspects: (1) labelling every individual scene pixel and (2) performing this task…
Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation
Naif Alshammari, Samet Akcay, Toby P. Breckon
Automotive scene understanding under adverse weather conditions raises a realistic and challenging problem attributable to poor outdoor scene visibility (e.g. foggy weather). Howev…
Trainable Structure Tensors for Autonomous Baggage Threat Detection Under Extreme Occlusion
Taimur Hassan, Samet Akcay, Mohammed Bennamoun +2
Detecting baggage threats is one of the most difficult tasks, even for expert officers. Many researchers have developed computer-aided screening systems to recognize these threats…
Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentation
Seyma Yucer, Samet Akçay, Noura Al-Moubayed +1
Whilst face recognition applications are becoming increasingly prevalent within our daily lives, leading approaches in the field still suffer from performance bias to the detriment…
Cascaded Structure Tensor Framework for Robust Identification of Heavily Occluded Baggage Items from X-ray Scans
Taimur Hassan, Samet Akcay, Mohammed Bennamoun +2
In the last two decades, baggage scanning has globally become one of the prime aviation security concerns. Manual screening of the baggage items is tedious, error-prone, and compro…
Cascaded Structure Tensor Framework for Robust Identification of Heavily Occluded Baggage Items from Multi-Vendor X-ray Scans
Taimur Hassan, Salman H. Khan, Samet Akcay +2
In the last two decades, luggage scanning has globally become one of the prime aviation security concerns. Manual screening of the baggage items is a cumbersome, subjective and ine…
Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging
Samet Akcay, Toby Breckon
X-ray security screening is widely used to maintain aviation/transport security, and its significance poses a particular interest in automated screening systems. This paper aims to…
Online Deep Reinforcement Learning for Autonomous UAV Navigation and Exploration of Outdoor Environments
Bruna G. Maciel-Pearson, Letizia Marchegiani, Samet Akcay +3
With the rapidly growing expansion in the use of UAVs, the ability to autonomously navigate in varying environments and weather conditions remains a highly desirable but as-of-yet…
Evaluating the Transferability and Adversarial Discrimination of Convolutional Neural Networks for Threat Object Detection and Classification within X-Ray Security Imagery
Yona Falinie A. Gaus, Neelanjan Bhowmik, Samet Akcay +1
X-ray imagery security screening is essential to maintaining transport security against a varying profile of threat or prohibited items. Particular interest lies in the automatic d…
On the Impact of Object and Sub-component Level Segmentation Strategies for Supervised Anomaly Detection within X-ray Security Imagery
Neelanjan Bhowmik, Yona Falinie A. Gaus, Samet Akcay +2
X-ray security screening is in widespread use to maintain transportation security against a wide range of potential threat profiles. Of particular interest is the recent focus on t…
Evaluation of a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery
Yona Falinie A. Gaus, Neelanjan Bhowmik, Samet Akçay +3
X-ray baggage security screening is widely used to maintain aviation and transport security. Of particular interest is the focus on automated security X-ray analysis for particular…
Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection
Samet Akçay, Amir Atapour-Abarghouei, Toby P. Breckon
Despite inherent ill-definition, anomaly detection is a research endeavor of great interest within machine learning and visual scene understanding alike. Most commonly, anomaly det…
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
Samet Akcay, Amir Atapour-Abarghouei, Toby P. Breckon
Anomaly detection is a classical problem in computer vision, namely the determination of the normal from the abnormal when datasets are highly biased towards one class (normal) due…