138 citations · 255 across the 9 of their papers we have counts for
11 papers · 1 filter
SAR Object Detection with Self-Supervised Pretraining and Curriculum-Aware Sampling
Yasin Almalioglu, Andrzej Kucik, Geoffrey French +3
Object detection in satellite-borne Synthetic Aperture Radar (SAR) imagery holds immense potential in tasks such as urban monitoring and disaster response. However, the inherent co…
Unsupervised Deep Persistent Monocular Visual Odometry and Depth Estimation in Extreme Environments
Yasin Almalioglu, Angel Santamaria-Navarro, Benjamin Morrell +1
In recent years, unsupervised deep learning approaches have received significant attention to estimate the depth and visual odometry (VO) from unlabelled monocular image sequences.…
VR-Caps: A Virtual Environment for Capsule Endoscopy
Kagan Incetan, Ibrahim Omer Celik, Abdulhamid Obeid +8
Current capsule endoscopes and next-generation robotic capsules for diagnosis and treatment of gastrointestinal diseases are complex cyber-physical platforms that must orchestrate…
EndoSLAM Dataset and An Unsupervised Monocular Visual Odometry and Depth Estimation Approach for Endoscopic Videos: Endo-SfMLearner
Kutsev Bengisu Ozyoruk, Guliz Irem Gokceler, Gulfize Coskun +12
Deep learning techniques hold promise to develop dense topography reconstruction and pose estimation methods for endoscopic videos. However, currently available datasets do not sup…
EndoL2H: Deep Super-Resolution for Capsule Endoscopy
Yasin Almalioglu, Kutsev Bengisu Ozyoruk, Abdulkadir Gokce +8
Although wireless capsule endoscopy is the preferred modality for diagnosis and assessment of small bowel diseases, the poor camera resolution is a substantial limitation for both…
SelfVIO: Self-Supervised Deep Monocular Visual-Inertial Odometry and Depth Estimation
Yasin Almalioglu, Mehmet Turan, Alp Eren Sari +4
In the last decade, numerous supervised deep learning approaches requiring large amounts of labeled data have been proposed for visual-inertial odometry (VIO) and depth map estimat…