Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Engineering
Spatio-Temporal Deep Learning Approaches For Addressing Track Association Problem Using Automatic Identification System (Ais) Data, Md Asif Bin Syed
Spatio-Temporal Deep Learning Approaches For Addressing Track Association Problem Using Automatic Identification System (Ais) Data, Md Asif Bin Syed
Graduate Theses, Dissertations, and Problem Reports
In the realm of marine surveillance, track association constitutes a pivotal yet challenging task, involving the identification and tracking of unlabelled vessel trajectories. The need for accurate data association algorithms stems from the urge to spot unusual vessel movements or threat detection. These algorithms link sequential observations containing location and motion information to specific moving objects, helping to build their real-time trajectories. These threat detection algorithms will be useful when a vessel attempts to conceal its identity. The algorithm can then identify and track the specific vessel from its incoming signal. The data for this study is sourced from the …
Deep Face Morph Detection Based On Wavelet Decomposition, Poorya Aghdaie
Deep Face Morph Detection Based On Wavelet Decomposition, Poorya Aghdaie
Graduate Theses, Dissertations, and Problem Reports
Morphed face images are maliciously used by criminals to circumvent the official process for receiving a passport where a look-alike accomplice embarks on requesting a passport. Morphed images are either synthesized by alpha-blending or generative networks such as Generative Adversarial Networks (GAN). Detecting morphed images is one of the fundamental problems associated with border control scenarios. Deep Neural Networks (DNN) have emerged as a promising solution for a myriad of applications such as face recognition, face verification, fake image detection, and so forth. The Biometrics communities have leveraged DNN to tackle fundamental problems such as morphed face detection. In this …