High-Speed Dynamic Scene Simulation In Ultraviolet To Infrared Spectra

1989 ◽  
Author(s):  
Ludwig G. Wolfert
Keyword(s):  
Infrared Spectra ◽  
High Speed ◽  
Dynamic Scene ◽  
Scene Simulation

Towards A Multi-FPGA Infrared Simulator

2007 ◽  
Vol 4 (4) ◽  
pp. 343-355 ◽  
Author(s):  
Vinay Sriram ◽  
David Kearney
Keyword(s):  
Homeland Security ◽  
Reconfigurable Computing ◽  
High Speed ◽  
High Performance ◽  
Large Scale ◽  
Computation Time ◽  
Ccd Camera ◽  
Hardware Acceleration ◽  
Limiting Factor ◽  
Scene Simulation

High speed infrared (IR) scene simulation is used extensively in defense and homeland security to test sensitivity of IR cameras and accuracy of IR threat detection and tracking algorithms used commonly in IR missile approach warning systems (MAWS). A typical MAWS requires an input scene rate of over 100 scenes/second. Infrared scene simulations typically take 32 minutes to simulate a single IR scene that accounts for effects of atmospheric turbulence, refraction, optical blurring and charge-coupled device (CCD) camera electronic noise on a Pentium 4 (2.8GHz) dual core processor [7]. Thus, in IR scene simulation, the processing power of modern computers is a limiting factor. In this paper we report our research to accelerate IR scene simulation using high performance reconfigurable computing. We constructed a multi Field Programmable Gate Array (FPGA) hardware acceleration platform and accelerated a key computationally intensive IR algorithm over the hardware acceleration platform. We were successful in reducing the computation time of IR scene simulation by over 36%. This research acts as a unique case study for accelerating large scale defense simulations using a high performance multi-FPGA reconfigurable computer.

scholarly journals Dynamic scene deblurring and image de-raining based on generative adversarial networks and transfer learning for Internet of vehicle

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Bingcai Wei ◽  
Liye Zhang ◽  
Kangtao Wang ◽  
Qun Kong ◽  
Zhuang Wang
Keyword(s):  
Transfer Learning ◽  
High Speed ◽  
Image Deblurring ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Dynamic Scene ◽  
The Real ◽  
Adversarial Networks ◽  
Rain Removal ◽  
Residual Block

AbstractExtracting traffic information from images plays an increasingly significant role in Internet of vehicle. However, due to the high-speed movement and bumps of the vehicle, the image will be blurred during image acquisition. In addition, in rainy days, as a result of the rain attached to the lens, the target will be blocked by rain, and the image will be distorted. These problems have caused great obstacles for extracting key information from transportation images, which will affect the real-time judgment of vehicle control system on road conditions, and further cause decision-making errors of the system and even have a bearing on traffic accidents. In this paper, we propose a motion-blurred restoration and rain removal algorithm for IoV based on generative adversarial network and transfer learning. Dynamic scene deblurring and image de-raining are both among the challenging classical research directions in low-level vision tasks. For both tasks, firstly, instead of using ReLU in a conventional residual block, we designed a residual block containing three 256-channel convolutional layers, and we used the Leaky-ReLU activation function. Secondly, we used generative adversarial networks for the image deblurring task with our Resblocks, as well as the image de-raining task. Thirdly, experimental results on the synthetic blur dataset GOPRO and the real blur dataset RealBlur confirm the effectiveness of our model for image deblurring. Finally, as an image de-raining task based on transfer learning, we can fine-tune the pre-trained model with less training data and show good results on several datasets used for image rain removal.

Research on Experimentation of Real-Time Dynamic Scene Simulation Based on UML

2013 ◽  
Vol 805-806 ◽  
pp. 1887-1890
Author(s):  
Ting Ting Jiang ◽  
Gang Xu ◽  
Jie Hu ◽  
Lu Lu
Keyword(s):  
Model Simulation ◽  
Simulation Models ◽  
Simulation Approach ◽  
Practical Application ◽  
Dynamic Scene ◽  
Scene Simulation ◽  
Time Dynamic ◽  
Operation Efficiency ◽  
Simulation Based ◽  
Actual Operation

Scene simulation technique has been widely used on the field of weapon developing. However, the problems such as technique intricacy and the difficulty of cooperating with work prevent it further developing. So the scene simulation approach to the complex system is put forward based on UML. It can be used to manage effectively the relation of scene simulation models with UML, the organization of models, and the maintenance or the modification of the simulation, to implement till the optimized project. Practical application showed that the approach is available for visualization of numerical calculation, simulation model, simulation interaction, maintenance and modification of scene simulation. It can improve actual operation efficiency considerable.

The optical system for infrared dynamic scene simulation

2005 ◽  
Author(s):  
Yawei Zheng ◽  
Jiaobo Gao ◽  
Jun Wang ◽  
Huiling Chen
Keyword(s):  
Optical System ◽  
Dynamic Scene ◽  
Scene Simulation

A Moving Object Detection Algorithm Based on ORB under Dynamic Scene

2014 ◽  
Vol 602-605 ◽  
pp. 1638-1641 ◽  
Author(s):  
Wen Hao Luo
Keyword(s):  
Object Detection ◽  
Difference Method ◽  
High Speed ◽  
Detection Algorithm ◽  
Moving Object Detection ◽  
Moving Object ◽  
Transformation Model ◽  
Dynamic Scene ◽  
Frame Difference ◽  
Inter Frame

In this thesis, a moving object detection algorithm under dynamic scene is proposed, which is based on ORB feature. Firstly, we extract feature points and match them by using ORB. We then obtain global motion compensation image by parameters of transformation matrix based on the RANSAC method. Finally, we use the inter-frame difference method to achieve the detection of moving targets. The high speed and accuracy of ORB feature point matching method, as well as the effectiveness of the RANSAC method for removing outliers ensure accurate calculation of parameters of affine transformation model. Combined with inter-frame difference method, foreground objects can be detected entirely. Experiment results show that the algorithm can accurately detect moving objects, and to some extent, it can solve the issue of real-time detection.

scholarly journals Dynamic Scene Deblurring and Image De-Raining Based on Generative Adversarial Networks and Transfer Learning for Internet of Vehicle

2021 ◽  
Author(s):  
Bingcai Wei ◽  
liye zhang ◽  
Kangtao Wang ◽  
Qun Kong ◽  
Zhuang Wang
Keyword(s):  
Transfer Learning ◽  
High Speed ◽  
Image Deblurring ◽  
Motion Blur ◽  
Generative Adversarial Networks ◽  
Dynamic Scene ◽  
Generative Adversarial Network ◽  
The Real ◽  
Adversarial Networks ◽  
Residual Block

Abstract Extracting traffic information from images plays an important role in Internet of Vehicle (IoV). However, due to the high-speed movement and bumpiness of the vehicle, motion blur will occur in image acquisition. In addition, in rainy days, because the rain is attached to the lens, the target will be blocked by rain, and the image will be distorted. These problems have caused great obstacles for extracting key information from transportation images, which will affect the real-time judgment of vehicle control system on road conditions, and further cause decision-making errors of the system and even cause traffic accidents. In this paper, we propose a motion blurred restoration and rain removal algorithm for IoV based on Generative Adversarial Network (GAN) and transfer learning. Dynamic scene deblurring and image de-raining are both among the challenging classical tasks in low-level vision tasks. For both tasks, firstly, instead of using ReLU in a conventional residual block, we designed a residual block containing three 256-channel convolutional layers, and we used the Leakly-ReLU activation function. Secondly, we used generative adversarial networks for the image deblurring task with our Resblock, as well as the image de-raining task. Thirdly, experimental results on the synthetic blur dataset GOPRO and the real blur dataset RealBlur confirm the effectiveness of our model for image deblurring. Finally, we can use the pre-trained model for the transfer learning-based image de-raining task and show good results on several datasets.

Multispectral dynamic scene simulation system for flying targets

2009 ◽  
Author(s):  
Zhangye Wang ◽  
Zuosheng Wang ◽  
Cheng Ye ◽  
Junwen Liang
Keyword(s):  
Simulation System ◽  
Dynamic Scene ◽  
Scene Simulation
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