机构

• 长春光学精密机械与物... [2]
• 软件研究所 [1]

采集方式

• OAI收割 [3]

内容类型

• 会议论文 [3]

发表日期

• 2006 [2]
• 2004 [1]

学科主题

筛选

浏览/检索结果: 共3条，第1-3条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Intelligent MRTD testing for thermal imaging system using ANN (EI CONFERENCE) 会议论文  OAI收割 ICO20: Remote Sensing and Infrared Devices and Systems, August 21, 2005 - August 26, 2005, Changchun, China Sun J.; Ma D. 收藏  |  浏览/下载：27/0  |  提交时间：2013/03/25 The Minimum Resolvable Temperature Difference (MRTD) is the most widely accepted figure for describing the performance of a thermal imaging system. Many models have been proposed to predict it. The MRTD testing is a psychophysical task  for which biases are unavoidable. It requires laboratory conditions such as normal air condition and a constant temperature. It also needs expensive measuring equipments and takes a considerable period of time. Especially when measuring imagers of the same type  the test is time consuming. So an automated and intelligent measurement method should be discussed. This paper adopts the concept of automated MRTD testing using boundary contour system and fuzzy ARTMAP  but uses different methods. It describes an Automated MRTD Testing procedure basing on Back-Propagation Network. Firstly  we use frame grabber to capture the 4-bar target image data. Then according to image gray scale  we segment the image to get 4-bar place and extract feature vector representing the image characteristic and human detection ability. These feature sets  along with known target visibility  are used to train the ANN (Artificial Neural Networks). Actually it is a nonlinear classification (of input dimensions) of the image series using ANN. Our task is to justify if image is resolvable or uncertainty. Then the trained ANN will emulate observer performance in determining MRTD. This method can reduce the uncertainties between observers and long time dependent factors by standardization. This paper will introduce the feature extraction algorithm  demonstrate the feasibility of the whole process and give the accuracy of MRTD measurement.   Study on color model conversion for camera with neural network based on the combination between second general revolving combination design and genetic algorithm (EI CONFERENCE) 会议论文  OAI收割 ICO20: Illumination, Radiation, and Color Technologies, August 21, 2005 - August 26, 2005, Changchun, China 作者:  Li Z.;  Zhou F.;  Wang C.;  Li Z. 收藏  |  浏览/下载：36/0  |  提交时间：2013/03/25 Munsell color system is selected to establish the mutual conversion between RGB and L*a*b* color model for camera. The color luminance meter and CCD camera synchronously measure the same color card  XYZ value is gotten from the color luminance meter  the training error is 0.000748566  it can show that the method combining second general revolving combination design with genetic algorithm can optimize the hidden-layer structure of neural network. Using the data of testing set to test this network and calculating the color difference between forecast value and true value  the color picture captured from CCD camera is expressed for RGB value as the input of neural network  and the L*a*b* value converted from XYZ value is regarded as the real color value of target card  which the difference is not obvious comparing with forecast result  the maximum is 5.6357 NBS  namely the output of neural network. The neural network of two hidden-layers is considered  the minimum is 0.5311 NBS  so the second general revolving combination design is introduced into optimizing the structure of neural network  and the average of color difference is 3.1744 NBS.  which can carry optimization through unifying project design  data processing and the precision of regression equation. Their mathematics model of encoding space is gained  and the significance inspection shows the confidence degree of regression equation is 99%. The mathematics model is optimized by genetic algorithm  optimization solution is gotten  and function value of the goal is 0.0007168. The neural network of the optimization solution is trained   path-oriented test data generation using symbolic execution and constraint solving techniques 会议论文  OAI收割 2nd International Conference on Software Engineering and Formal Methods, Beijing, PEOPLES R CHINA, SEP 28-30, Zhang J; Xu C; Wang XL   |  收藏  |  浏览/下载：23/0  |  提交时间：2011/07/29 automatic test data generation  breadth-first search  constraint solving  depth-first search  extended finite state machine  input program  input test data  input variables  path-oriented test data generation  program bug  software engineering  symbolic e