Virtual reality with motion parallax by dense optical flow-based depth generation from two spherical images

2017 ◽  
Author(s):  
Sarthak Pathak ◽  
Alessandro Moro ◽  
Hiromitsu Fujii ◽  
Atsushi Yamashita ◽  
Hajime Asama
Keyword(s):  
Virtual Reality ◽  
Optical Flow ◽  
Motion Parallax ◽  
Dense Optical Flow ◽  
Spherical Images

MODIFIED METHOD FOR REAL-TIME COMPUTING DENSE OPTICAL FLOW ON FPGA

2019 ◽  
Vol 70 ◽  
pp. 146-157
Author(s):  
A. V. Bratulin ◽  
◽  
M. B. Nikiforov ◽  
A. I. Efimov ◽  
◽  
...  
Keyword(s):  
Optical Flow ◽  
Real Time ◽  
Modified Method ◽  
Dense Optical Flow ◽  
Real Time Computing

Image Stabilization Based on Panoramic Dense Optical Flow Matching for Camera on Flapping Wing

2021 ◽  
Author(s):  
Tian Shen ◽  
Cui Long ◽  
Liu Zhaoming ◽  
Wang Hongwei ◽  
Zhang Feng ◽  
...  
Keyword(s):  
Optical Flow ◽  
Flapping Wing ◽  
Image Stabilization ◽  
Dense Optical Flow

Does the Management of Visual and Audible Motion Information during an Immersive Virtual Reality Timed Up and Go Test Impact Locomotor Performance in the Elderly?

Gerontology ◽  
2021 ◽  
pp. 1-9
Author(s):  
Frédéric Muhla ◽  
Karine Duclos ◽  
Fabien Clanché ◽  
Philippe Meyer ◽  
Séverine Maïaux ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Motor Control ◽  
Optical Flow ◽  
Fear Of Falling ◽  
The Elderly ◽  
Public Health Issue ◽  
Immersive Virtual Reality ◽  
Timed Up And Go ◽  
Friedman's Test ◽  
Risk Of Falls

<b><i>Background/Aims:</i></b> Falling among the elderly is a major public health issue, especially with the advancing age of the baby boomers. The fall risk assessment tests for many lack a context that would bring them closer to everyday life. Thus, immersive virtual reality, which makes it possible to simulate everyday situations, could make it possible to strengthen the quality of the assessment of the risk of falls. However, it is necessary to understand how the use of a virtual reality device influences the motor control of elderly participants. If vestibular physiotherapists use VR to virtualize their tools, what impact would a visual simulation of movement have on motor control in a locomotor task, if this simulation were plausible? <b><i>Methods:</i></b> Sixty-two elders (70.8 ± 6.7 years old) completed a Timed Up and Go task under 3 conditions: real, virtual reality, and virtual reality with visual and sound movement information. The virtual reality task takes place in a train either stationary at a station or in uniform linear motion. The time and number of steps were recorded using video, and comparisons between conditions were made using Friedman’s test. <b><i>Results:</i></b> The results show a significant increase in the time and number of steps in “virtual reality” condition compared to the “real” condition. They do not show significant differences between the 2 virtual conditions. <b><i>Conclusion:</i></b> The use of a running virtual train to provide plausible movement is particularly distinguished from vestibular physiotherapy applications with first a fixed visual support partially obscuring the optical flow. This visual aid coupled with the attention dedicated to the task inhibits the effect of the moving environment on locomotion. However, the visual optical flow will potentially have an effect in people with fear of falling. Virtual reality shows great potential for the simulation of realistic environments for the assessment of the risk of falls and opens up avenues for the development of tests.

scholarly journals A Hardware-Friendly Optical Flow-Based Time-to-Collision Estimation Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 807
Author(s):  
Cong Shi ◽  
Zhuoran Dong ◽  
Shrinivas Pundlik ◽  
Gang Luo
Keyword(s):  
Embedded System ◽  
Optical Flow ◽  
Random Forests ◽  
Visual Motion ◽  
Hardware Acceleration ◽  
Ground Truth ◽  
Estimation Algorithm ◽  
Estimation Accuracy ◽  
Time To Collision ◽  
Dense Optical Flow

This work proposes a hardware-friendly, dense optical flow-based Time-to-Collision (TTC) estimation algorithm intended to be deployed on smart video sensors for collision avoidance. The algorithm optimized for hardware first extracts biological visual motion features (motion energies), and then utilizes a Random Forests regressor to predict robust and dense optical flow. Finally, TTC is reliably estimated from the divergence of the optical flow field. This algorithm involves only feed-forward data flows with simple pixel-level operations, and hence has inherent parallelism for hardware acceleration. The algorithm offers good scalability, allowing for flexible tradeoffs among estimation accuracy, processing speed and hardware resource. Experimental evaluation shows that the accuracy of the optical flow estimation is improved due to the use of Random Forests compared to existing voting-based approaches. Furthermore, results show that estimated TTC values by the algorithm closely follow the ground truth. The specifics of the hardware design to implement the algorithm on a real-time embedded system are laid out.

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