activity
20172021
most citedDeep EndoVO: A Recurrent Convolutional Neural Network (RCNN) based Visual Odometry Approach for Endoscopic Capsule Robots

138 citations · 459 across the 15 of their papers we have counts for

collaborators
Showing cs.CVShow all

10 papers · 1 filter

cs.CV2020

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…

cs.CV2020★ 5 cited

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…

cs.CV2020

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…

cs.CV2019★ 11 cited

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…

cs.CV2017★ 138 cited

Deep EndoVO: A Recurrent Convolutional Neural Network (RCNN) based Visual Odometry Approach for Endoscopic Capsule Robots

Mehmet Turan, Yasin Almalioglu, Helder Araujo +2

Ingestible wireless capsule endoscopy is an emerging minimally invasive diagnostic technology for inspection of the GI tract and diagnosis of a wide range of diseases and pathologi…

cs.CV2017★ 36 cited

Sparse-then-Dense Alignment based 3D Map Reconstruction Method for Endoscopic Capsule Robots

Mehmet Turan, Yusuf Yigit Pilavci, Ipek Ganiyusufoglu +3

Since the development of capsule endoscopcy technology, substantial progress were made in converting passive capsule endoscopes to robotic active capsule endoscopes which can be co…

cs.CV2017★ 20 cited

A fully dense and globally consistent 3D map reconstruction approach for GI tract to enhance therapeutic relevance of the endoscopic capsule robot

Mehmet Turan, Yusuf Yigit Pilavci, Redhwan Jamiruddin +3

In the gastrointestinal (GI) tract endoscopy field, ingestible wireless capsule endoscopy is emerging as a novel, minimally invasive diagnostic technology for inspection of the GI…

cs.CV2017★ 61 cited

A Non-Rigid Map Fusion-Based RGB-Depth SLAM Method for Endoscopic Capsule Robots

Mehmet Turan, Yasin Almalioglu, Helder Araujo +2

In the gastrointestinal (GI) tract endoscopy field, ingestible wireless capsule endoscopy is considered as a minimally invasive novel diagnostic technology to inspect the entire GI…

cs.CV2017★ 27 cited

A Deep Learning Based 6 Degree-of-Freedom Localization Method for Endoscopic Capsule Robots

Mehmet Turan, Yasin Almalioglu, Ender Konukoglu +1

We present a robust deep learning based 6 degrees-of-freedom (DoF) localization system for endoscopic capsule robots. Our system mainly focuses on localization of endoscopic capsul…

cs.CV2017★ 9 cited

Magnetic-Visual Sensor Fusion based Medical SLAM for Endoscopic Capsule Robot

Mehmet Turan, Yasin Almalioglu, Hunter Gilbert +3

A reliable, real-time simultaneous localization and mapping (SLAM) method is crucial for the navigation of actively controlled capsule endoscopy robots. These robots are an emergin…