机构

• 长春光学精密机械与物... [3]

采集方式

• OAI收割 [3]

内容类型

• 会议论文 [3]

发表日期

• 2012 [2]
• 2006 [1]

学科主题

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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 Design of the real-time autofocusing system for collimator with long focus length and large aperture (EI CONFERENCE) 会议论文  OAI收割 2011 International Academic Conference on Numbers, Intelligence, Manufacturing Technology and Machinery Automation, MAMT 2011, December 24, 2011 - December 25, 2011, Wuhan, China 作者:  Zhang X.;  Zhang X.;  Zhang X. 收藏  |  浏览/下载：42/0  |  提交时间：2013/03/25 Large aperture collimator which has been widely used for calibrating and testing various optical devices plays an essential role in correlative laboratories. As being the basic testing and calibration equipment  the large aperture collimator's accuracy should be much higher than the device under testing in order to ensure the accuracy of the measurement. However  the process of adjusting the collimator is extremely complicated due to the collimator's large aperture and long focal length. So it is difficult to ensure the measurement's quality and easy to cause the system being vulnerable to the surrounding environment. One of the most common problems is defocus. In order to solve the problem above  this issue presents a new type of autocollimator autofocusing system which uses pentaprism instead of using large-aperture plane mirror  semiconductor lasers as light source and CCD sensor as receiver. The system is smaller  lighter  and more convenient when using. The computer simulation shows that the autofocusing system's resolution could reach the accuracy of 40m. If we use the relevant algorithms to execute the sub-pixel scanning  the resolution could reach the accuracy of 10m. It shows that the system could satisfy the required testing precision of testing large aperture optical device.   Classification of hyperspectral image based on SVM optimized by a new particle swarm optimization (EI CONFERENCE) 会议论文  OAI收割 2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2012, June 1, 2012 - June 3, 2012, Nanjing, China 作者:  Gao X.;  Yu P.;  Yu P. 收藏  |  浏览/下载：22/0  |  提交时间：2013/03/25 Support Vector Machine (SVM) is used to classify hyperspectral remote sensing image in this paper. Radial Basis Function (RBF)  which is most widely used  is chosen as the kernel function of SVM. Selection of kernel function parameter is a pivotal factor which influences the performance of SVM. For this reason  Particle Swarm Optimization (PSO) is provided to get a better result. In order to improve the optimization efficiency of kernel function parameter  firstly larger steps of grid search method is used to find the appropriate rang of parameter. Since the PSO tends to be trapped into local optimal solutions  a weight and mutation particle swam optimization algorithm was proposed  in which the weight dynamically changes with a liner rule and the global best particle mutates per iteration to optimize the parameters of RBF-SVM. At last  a 220-bands hyperspectral remote sensing image of AVIRIS is taken as an experiment  which demonstrates that the method this paper proposed is an effective way to search the SVM parameters and is available in improving the performance of SVM classifiers. 2012 IEEE.   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.