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	The method on the measurement of the aircraft attitude by the spatial cosines relationship of the single station and planes to the intersection the multi-station of electro-optical theodolite (EI CONFERENCE) 会议论文 OAI收割 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, CMCE 2010, August 24, 2010 - August 26, 2010, Changchun, China Zhao L.-R.; Cao Y.-G. 收藏 \| 浏览/下载：16/0 \| 提交时间：2013/03/25 In order to realize the aircraft attitude measurement The paper advances electro-optical theodolite methods of measuring the aircraft attitude by using the spatial cosines relationship the single station theodolite and how to use intersection of the multi-station theodolite. The single station theodolite was introduced in a measured distance from the aircraft posture information. An exhaustive approach was used to access to the aircraft axis of the feature points and the location in the space was used to obtain space posture parameters. The multi-station theodolite was introduced to get the information of the aircraft flight attitude by method of using planes to the intersection. Two-dimensional image to obtain objective axis by fitting algorithm using Hough transform. And then Calculate the vertical distance of the origin to the target of the central axis and the origin to the target of the central axis of the angle between the normal and the X-axis obtaining the linear equation in the two-dimensional plane. Two-dimensional axis of the target image and the camera system's optical center only identified a space plane. The aircraft yaw axis angle and pitch angle can be obtained by getting the space axis using plains to the intersection method. Experimental results show that attitude angle error of the single station theodolite is less than 1 when its distance is less than 6000m and attitude angle error of the multi-station theodolite is less than 0.6 when its intersection angle is between 30 and 150. In this paper according to test result of precision comparing the mathematical model is correct and the algorithm is reasonable in extracting effective parameters of the aircraft's attitude. 2010 IEEE.
	Development of Laser Stripe Sensor for Automatic Seam Tracking in Robotic Tailored Blank Welding 会议论文 OAI收割 7th World Congress on Intelligent Control and Automation, Chongqing, China, June 25-27, 2008 作者: Zou YY(邹媛媛); Zhao MY(赵明扬); Zhang L(张雷); Jiang CY(姜春英) 收藏 \| 浏览/下载：19/0 \| 提交时间：2012/06/06 laser strip sensor seam tracking tailored blank welding error analysis feature points extracting
	A segment detection method based on improved Hough transform (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China 作者: Yao Z.-J. 收藏 \| 浏览/下载：26/0 \| 提交时间：2013/03/25 Hough transform is recognized as a powerful tool in shape analysis which gives good results even in the presence of noise and the disconnection of edge. However 3. applying the standard Hough transform equation to every point of the input image edge 4. according to the local threshold 6. merging the segments whose extreme points are near. Experiment results show the approach not only can recognize regular geometric object but also can extract the segment feature of real targets in complex environment. So the proposed method can be used in the target detection of complicated scenes traditional Hough transform can only detect the lines 2. quantizing the parameter space and extracting a group of maximums according to the global threshold eliminating spurious peaks which are caused by the spreading effects and will improve the precision of tracking. cannot give the endpoints and length of the line segments and it is vulnerable to the quantization errors. Based on the analysis of its limitations Hough transform has been improved in order to detect line segment feature of targets. The algorithm aims to avoid the loss of spatial information as well as to eliminate the spurious peaks and fix on the line segments endpoints accurately 5. fixing on the endpoints of the segments according to the dynamic clustering rule which can expediently be used for the description and classification of regular objects. The method consists of 6 steps: 1. setting up the image parameter and line-segment spaces