no code implementations • 18 Apr 2024 • Yona Falinie A. Gaus, Neelanjan Bhowmik, Brian K. S. Isaac-Medina, Toby P. Breckon
Our results show that SAM can segment objects in the X-ray modality when given a box prompt, but its performance varies for point prompts.
1 code implementation • ICCV 2023 • Abril Corona-Figueroa, Sam Bond-Taylor, Neelanjan Bhowmik, Yona Falinie A. Gaus, Toby P. Breckon, Hubert P. H. Shum, Chris G. Willcocks
Generating 3D images of complex objects conditionally from a few 2D views is a difficult synthesis problem, compounded by issues such as domain gap and geometric misalignment.
no code implementations • 2 Mar 2023 • Jack W Barker, Neelanjan Bhowmik, Yona Falinie A Gaus, Toby P Breckon
Anomaly detection is the task of recognising novel samples which deviate significantly from pre-establishednormality.
no code implementations • 24 Nov 2022 • Rushikesh Zawar, Krupa Bhayani, Neelanjan Bhowmik, Kamlesh Tiwari, Dhiraj Sangwan
Most of the available data in the anomaly detection task is imbalanced as the number of positive/anomalous instances is sparse.
no code implementations • 24 Nov 2022 • Benjamin Kiefer, Matej Kristan, Janez Perš, Lojze Žust, Fabio Poiesi, Fabio Augusto de Alcantara Andrade, Alexandre Bernardino, Matthew Dawkins, Jenni Raitoharju, Yitong Quan, Adem Atmaca, Timon Höfer, Qiming Zhang, Yufei Xu, Jing Zhang, DaCheng Tao, Lars Sommer, Raphael Spraul, Hangyue Zhao, Hongpu Zhang, Yanyun Zhao, Jan Lukas Augustin, Eui-ik Jeon, Impyeong Lee, Luca Zedda, Andrea Loddo, Cecilia Di Ruberto, Sagar Verma, Siddharth Gupta, Shishir Muralidhara, Niharika Hegde, Daitao Xing, Nikolaos Evangeliou, Anthony Tzes, Vojtěch Bartl, Jakub Špaňhel, Adam Herout, Neelanjan Bhowmik, Toby P. Breckon, Shivanand Kundargi, Tejas Anvekar, Chaitra Desai, Ramesh Ashok Tabib, Uma Mudengudi, Arpita Vats, Yang song, Delong Liu, Yonglin Li, Shuman Li, Chenhao Tan, Long Lan, Vladimir Somers, Christophe De Vleeschouwer, Alexandre Alahi, Hsiang-Wei Huang, Cheng-Yen Yang, Jenq-Neng Hwang, Pyong-Kun Kim, Kwangju Kim, Kyoungoh Lee, Shuai Jiang, Haiwen Li, Zheng Ziqiang, Tuan-Anh Vu, Hai Nguyen-Truong, Sai-Kit Yeung, Zhuang Jia, Sophia Yang, Chih-Chung Hsu, Xiu-Yu Hou, Yu-An Jhang, Simon Yang, Mau-Tsuen Yang
The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection.
no code implementations • 29 Oct 2022 • Neelanjan Bhowmik, Toby P. Breckon
X-ray baggage security screening is in widespread use and crucial to maintaining transport security for threat/anomaly detection tasks.
no code implementations • 16 May 2022 • Neelanjan Bhowmik, Jack W. Barker, Yona Falinie A. Gaus, Toby P. Breckon
When training and evaluating on uncompressed data as a baseline, we achieve maximal mean Average Precision (mAP) of 0. 823 with Cascade R-CNN across the FLIR dataset, outperforming prior work.
no code implementations • 1 Dec 2021 • Jack. W. Barker, Neelanjan Bhowmik, Toby. P. Breckon
Within commercial wind energy generation, the monitoring and predictive maintenance of wind turbine blades in-situ is a crucial task, for which remote monitoring via aerial survey from an Unmanned Aerial Vehicle (UAV) is commonplace.
no code implementations • 10 Oct 2021 • Thomas W. Webb, Neelanjan Bhowmik, Yona Falinie A. Gaus, Toby P. Breckon
The recent advancement in deep Convolutional Neural Network (CNN) has brought insight into the automation of X-ray security screening for aviation security and beyond.
no code implementations • 27 Aug 2021 • Neelanjan Bhowmik, Yona Falinie A. Gaus, Toby P. Breckon
Automatic detection of prohibited items within complex and cluttered X-ray security imagery is essential to maintaining transport security, where prior work on automatic prohibited item detection focus primarily on pseudo-colour (rgb}) X-ray imagery.
2 code implementations • 17 Oct 2020 • William Thomson, Neelanjan Bhowmik, Toby P. Breckon
Automatic visual fire detection is used to complement traditional fire detection sensor systems (smoke/heat).
no code implementations • 3 Aug 2020 • Qian Wang, Neelanjan Bhowmik, Toby P. Breckon
X-ray Computed Tomography (CT) based 3D imaging is widely used in airports for aviation security screening whilst prior work on automatic prohibited item detection focus primarily on 2D X-ray imagery.
no code implementations • 27 Mar 2020 • Qian Wang, Neelanjan Bhowmik, Toby P. Breckon
As the first attempt to use 3D CNN for volumetric 3D CT baggage security screening, we first evaluate different CNN architectures on the classification of isolated prohibited item volumes and compare against traditional methods which use hand-crafted features.
no code implementations • 20 Nov 2019 • Ganesh Samarth C. A., Neelanjan Bhowmik, Toby P. Breckon
In this work we explore different Convolutional Neural Network (CNN) architectures and their variants for non-temporal binary fire detection and localization in video or still imagery.
no code implementations • 20 Nov 2019 • Yona Falinie A. Gaus, Neelanjan Bhowmik, Samet Akcay, Toby P. Breckon
X-ray imagery security screening is essential to maintaining transport security against a varying profile of threat or prohibited items.
no code implementations • 19 Nov 2019 • Neelanjan Bhowmik, Yona Falinie A. Gaus, Samet Akcay, Jack W. Barker, Toby P. Breckon
X-ray security screening is in widespread use to maintain transportation security against a wide range of potential threat profiles.
no code implementations • 25 Sep 2019 • Neelanjan Bhowmik, Qian Wang, Yona Falinie A. Gaus, Marcin Szarek, Toby P. Breckon
This work opens up the possibility of using synthetically composed imagery, avoiding the need to collate such large volumes of hand-annotated real-world imagery.
no code implementations • 10 Apr 2019 • Yona Falinie A. Gaus, Neelanjan Bhowmik, Samet Akçay, Paolo M. Guillen-Garcia, Jack W. Barker, Toby P. Breckon
Subsequently, leveraging a range of established CNN object and fine-grained category classification approaches we formulate within object anomaly detection as a two-class problem (anomalous or benign).