no code implementations • 2 Sep 2023 • Steven Ndung'u, Trienko Grobler, Stefan J. Wijnholds, Dimka Karastoyanova, George Azzopardi
In this work, we utilize deep hashing to rapidly search for similar images in a large database.
no code implementations • 5 May 2023 • Steven Ndung'u, Trienko Grobler, Stefan J. Wijnholds, Dimka Karastoyanova, George Azzopardi
Nonetheless, this is only plausible with the exploitation of intensive machine intelligence to complement human-aided and traditional statistical techniques.
1 code implementation • Journal of Open Source Software 2022 • Jeroen G. S. Overschie, Ahmad Alsahaf, George Azzopardi
The package is open source and can be installed through PyPI.
no code implementations • 14 Jun 2022 • Anusha Aswath, Ahmad Alsahaf, Ben N. G. Giepmans, George Azzopardi
Automated and semi-automated techniques in biomedical electron microscopy (EM) enable the acquisition of large datasets at a high rate.
no code implementations • 8 Jun 2022 • Fadi Mohsen, Dimka Karastoyanova, George Azzopardi
In turn, our approach can support developers in improving their apps and users in downloading the ones that are less likely to be removed.
no code implementations • 1 Jan 2021 • Nicola Strisciuglio, George Azzopardi, Nicolai Petkov
The rectified responses of the push and pull filter pairs are then combined by a linear function.
no code implementations • 11 Dec 2020 • Derrick Timmerman, Swaroop Bennabhaktula, Enrique Alegre, George Azzopardi
In this work we propose a method to identify the source camera of a video based on camera specific noise patterns that we extract from video frames.
no code implementations • 23 Apr 2020 • Guru Swaroop Bennabhaktula, Enrique Alegre, Dimka Karastoyanova, George Azzopardi
One of the challenging problems in digital image forensics is the capability to identify images that are captured by the same camera device.
1 code implementation • 26 Nov 2018 • Nicola Strisciuglio, George Azzopardi, Nicolai Petkov
This type of inhibition allows for sharper detection of the patterns of interest and improves the quality of delineation especially in images with spurious texture.
no code implementations • 24 Jul 2017 • Nicola Strisciuglio, George Azzopardi, Nicolai Petkov
The detection of curvilinear structures is an important step for various computer vision applications, ranging from medical image analysis for segmentation of blood vessels, to remote sensing for the identification of roads and rivers, and to biometrics and robotics, among others.