Automatic Re-orientation of 3D Echocardiographic Images in Virtual Reality Using Deep Learning

2021 ◽  
pp. 177-188
Author(s):  
Lindsay Munroe ◽  
Gina Sajith ◽  
Ei Lin ◽  
Surjava Bhattacharya ◽  
Kuberan Pushparajah ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Deep Learning ◽  
Echocardiographic Images

scholarly journals Correction to: Predicting user visual attention in virtual reality with a deep learning model

2021 ◽  
Author(s):  
Xiangdong Li ◽  
Yifei Shan ◽  
Wenqian Chen ◽  
Yue Wu ◽  
Preben Hansen ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Deep Learning ◽  
Visual Attention ◽  
Learning Model ◽  
Deep Learning Model

scholarly journals Research on Efficient Deep Learning Algorithm Based on ShuffleGhost in the Field of Virtual Reality

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Bangtong Huang ◽  
Hongquan Zhang ◽  
Zihong Chen ◽  
Lingling Li ◽  
Lihua Shi
Keyword(s):  
Virtual Reality ◽  
Deep Learning ◽  
Large Scale ◽  
Learning Algorithm ◽  
Feature Maps ◽  
Embedded Devices ◽  
Feature Map ◽  
Deep Learning Algorithm ◽  
Proper Design ◽  
The Cost

Deep learning algorithms are facing the limitation in virtual reality application due to the cost of memory, computation, and real-time computation problem. Models with rigorous performance might suffer from enormous parameters and large-scale structure, and it would be hard to replant them onto embedded devices. In this paper, with the inspiration of GhostNet, we proposed an efficient structure ShuffleGhost to make use of the redundancy in feature maps to alleviate the cost of computations, as well as tackling some drawbacks of GhostNet. Since GhostNet suffers from high computation of convolution in Ghost module and shortcut, the restriction of downsampling would make it more difficult to apply Ghost module and Ghost bottleneck to other backbone. This paper proposes three new kinds of ShuffleGhost structure to tackle the drawbacks of GhostNet. The ShuffleGhost module and ShuffleGhost bottlenecks are utilized by the shuffle layer and group convolution from ShuffleNet, and they are designed to redistribute the feature maps concatenated from Ghost Feature Map and Primary Feature Map. Besides, they eliminate the gap of them and extract the features. Then, SENet layer is adopted to reduce the computation cost of group convolution, as well as evaluating the importance of the feature maps which concatenated from Ghost Feature Maps and Primary Feature Maps and giving proper weights for the feature maps. This paper conducted some experiments and proved that the ShuffleGhostV3 has smaller trainable parameters and FLOPs with the ensurance of accuracy. And with proper design, it could be more efficient in both GPU and CPU side.

Virtual reality rendering methods for training deep learning, analysing landscapes, and preventing virtual reality sickness

2020 ◽  
pp. 147807712095754
Author(s):  
Tomohiro Fukuda ◽  
Marcos Novak ◽  
Hiroyuki Fujii ◽  
Yoann Pencreach
Keyword(s):  
Virtual Reality ◽  
Deep Learning ◽  
Urban Design ◽  
Architectural Design ◽  
Design Studies ◽  
Virtual Camera ◽  
Use Of Data ◽  
Shadow Casting ◽  
Angular Velocities ◽  
Velocity Information

Virtual reality (VR) has been proposed for various purposes such as design studies, presentation, simulation and communication in the field of computer-aided architectural design. This paper explores new roles for VR; in particular, we propose rendering methods that consist of post-processing rendering, segmentation rendering and shadow-casting rendering for more-versatile approaches in the use of data. We focus on the creation of a dataset of annotated images, composed of paired foreground-background and semantic-relevant images, in addition to traditional immersive rendering for training deep learning neural networks and analysing landscapes. We also develop a camera velocity rendering method using a customised segmentation rendering technique that calculates the linear and angular velocities of the virtual camera within the VR space at each frame and overlays a colour on the screen according to the velocity value. Using this velocity information, developers of VR applications can improve the animation path within the VR space and prevent VR sickness. We successfully applied the developed methods to urban design and a design project for a building complex. In conclusion, the proposed method was evaluated to be both feasible and effective.

scholarly journals Using EEG and Deep Learning to Predict Motion Sickness Under Wearing a Virtual Reality Device

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 126784-126796
Author(s):  
Chung-Yen Liao ◽  
Shao-Kuo Tai ◽  
Rung-Ching Chen ◽  
Hendry Hendry
Keyword(s):  
Virtual Reality ◽  
Deep Learning ◽  
Motion Sickness
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