Real Time Terrain Image Generation

1983 ◽  
Author(s):  
J.William Weber
Keyword(s):  
Author(s):  
Hyung-Hwa Ko ◽  
GeunTae Kim ◽  
Hyunmin Kim

Since deep learning applications in object recognition, object detection, segmentation, and image generation are needed increasingly, related research has been actively conducted. In this paper, using segmentation and style transfer together, a method of producing desired images in the desired area in real-time video is proposed. Two deep neural networks were used to enable as possible as in real-time with the trade-off relationship between speed and accuracy. Modified BiSeNet for segmentation and CycleGAN for style transfer were processed on a desktop PC equipped with two RTX-2080-Ti GPU boards. This enables real-time processing over SD video in decent level. We obtained good results in subjective quality to segment Road area in city street video and change into the Grass style at no less than 6(fps).


1990 ◽  
Vol 36 (3) ◽  
pp. 216-222
Author(s):  
H. Ishida ◽  
N. Iwata ◽  
K. Nemoto

2014 ◽  
Vol 1030-1032 ◽  
pp. 1692-1695
Author(s):  
Xiu Zhi Zhou ◽  
Zhan Ping Fu ◽  
Zhi Le Wang

Common Image Generation Interface is the interface between two real-time communication subsystems. In order to exchange data between control end and rendering end of the scene simulation system, a packaging component based on common image generation interface is proposed in this paper, which is applied to visual simulation of helicopters and fighter. The simulation results show that this method can meet the real-time requirement of visual control system and is valuable in visual simulation of the weapon equipment operational field.


2013 ◽  
Vol 415 ◽  
pp. 012045 ◽  
Author(s):  
G Wetzstein ◽  
D Lanman ◽  
M Hirsch ◽  
R Raskar

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