Real-Time 3D Depth Generation for Stereoscopic Video Applications with Thread-Level Superscalar-Pipeline Parallelization

2012 ◽  
Vol 72 (1) ◽  
pp. 17-33
Author(s):  
Guo-An Jian ◽  
Cheng-An Chien ◽  
Peng-Sheng Chen ◽  
Jiun-In Guo
Keyword(s):  
Real Time ◽  
Stereoscopic Video ◽  
Superscalar Pipeline

Augmented Reality During Robot-assisted Laparoscopic Partial Nephrectomy: Toward Real-Time 3D-CT to Stereoscopic Video Registration

Urology ◽  
2009 ◽  
Vol 73 (4) ◽  
pp. 896-900 ◽  
Author(s):  
Li-Ming Su ◽  
Balazs P. Vagvolgyi ◽  
Rahul Agarwal ◽  
Carol E. Reiley ◽  
Russell H. Taylor ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Partial Nephrectomy ◽  
Laparoscopic Partial Nephrectomy ◽  
3D Ct ◽  
Stereoscopic Video ◽  
Robot Assisted ◽  
Video Registration

scholarly journals Real-time Temporal Synchronization and Compensation in Stereoscopic Video

2013 ◽  
Vol 18 (5) ◽  
pp. 680-690 ◽  
Author(s):  
Giseok Kim ◽  
Jae-Soo Cho ◽  
Gwangsoon Lee ◽  
Eung-Don Lee
Keyword(s):  
Real Time ◽  
Stereoscopic Video ◽  
Temporal Synchronization

An media aware platform for real-time stereoscopic video streaming adaptation

2013 ◽  
Author(s):  
Athanasios Kordelas ◽  
Ilias Politis ◽  
Asimakis Lykourgiotis ◽  
Tasos Dagiuklas ◽  
Stavros Kotsopoulos
Keyword(s):  
Real Time ◽  
Video Streaming ◽  
Stereoscopic Video

An Innovative No-Reference Metric for Real-Time 3D Stereoscopic Video Quality Assessment

2016 ◽  
Vol 62 (3) ◽  
pp. 654-663 ◽  
Author(s):  
Yi Han ◽  
Zhenhui Yuan ◽  
Gabriel-Miro Muntean
Keyword(s):  
Quality Assessment ◽  
Real Time ◽  
Video Quality ◽  
Video Quality Assessment ◽  
Stereoscopic Video

Development of single-channel stereoscopic video imaging modality for real-time retinal imaging

2016 ◽  
Author(s):  
Edalat Radfar ◽  
Jihoon Park ◽  
Sangyeob Lee ◽  
Myungjin Ha ◽  
Sungkon Yu ◽  
...  
Keyword(s):  
Real Time ◽  
Single Channel ◽  
Imaging Modality ◽  
Retinal Imaging ◽  
Stereoscopic Video ◽  
Video Imaging

Design of 3D-TV Horizontal Parallax Obtaining System Based on FPGA

2013 ◽  
Vol 401-403 ◽  
pp. 1834-1838
Author(s):  
Jia Xi Yu ◽  
Wen Hui Zhang
Keyword(s):  
Real Time ◽  
Digital Tv ◽  
Entire System ◽  
Stereoscopic Video ◽  
Control Center ◽  
3D Tv

In this paper, a design of FPGA-based 3D-TV horizontal parallax acquiring system is presented. The system will receive the stereoscopic video by a HD-SDI receiver GS2971, and outputs a video of horizontal parallax to a digital TV through a HDMI transmitter SiI9134. In this system, FPGA plays an important role that converts the stereoscopic video to the horizontal parallax video. In addition, a microcontroller is selected as the control center of the entire system. This system can get the horizontal parallax of the stereoscopic video in real time, and is helpful for the stereoscopic program producer to control the horizontal parallax of the 3D program.

scholarly journals Stereo Vision Based Robot for Remote Monitoring with VR Support

2019 ◽  
Vol 9 (1S3) ◽  
pp. 79-84
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Stereo Vision ◽  
Deep Neural Networks ◽  
Moving Objects ◽  
Video Stream ◽  
Head Movements ◽  
Depth Information ◽  
Stereoscopic Video ◽  
Cloud Server

The machine vision systems have been playing a significant role in visual monitoring systems. With the help of stereovision and machine learning, it will be able to mimic human-like visual system and behaviour towards the environment. In this paper, we present a stereo vision based 3-DOF robot which will be used to monitor places from remote using cloud server and internet devices. The 3-DOF robot will transmit human-like head movements, i.e., yaw, pitch, roll and produce 3D stereoscopic video and stream it in Real-time. This video stream is sent to the user through any generic internet devices with VR box support, i.e., smartphones giving the user a First-person real-time 3D experience and transfers the head motion of the user to the robot also in Real-time. The robot will also be able to track moving objects and faces as a target using deep neural networks which enables it to be a standalone monitoring robot. The user will be able to choose specific subjects to monitor in a space. The stereovision enables us to track the depth information of different objects detected and will be used to track human interest objects with its distances and sent to the cloud. A full working prototype is developed which showcases the capabilities of a monitoring system based on stereo vision, robotics, and machine learning.

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