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	基于消失点检测算法的束靶红外图像畸变校正期刊论文 OAI收割强激光与粒子束, 2023, 卷号: 35 作者: 陈丽萍; 许永建; 於子辰; 汪日新; 彭旭峰 \| 收藏 \| 浏览/下载：18/0 \| 提交时间：2023/11/10 vanishing point detection linear detection image rectification line segment clustering neutral beam diagnosis 消失点检测直线检测图像校正线段聚类中性束诊断
	Rail Detection Based on LSD and the Least Square Curve Fitting 期刊论文 OAI收割 International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 1, 页码: 85-95 作者: Yun-Shui Zheng; Yan-Wei Jin \| 收藏 \| 浏览/下载：23/0 \| 提交时间：2021/02/23 Rail inspection line segment detector (LSD) algorithm the least square curve fitting foreign object detection
	Step-by-step pipeline processing approach for line segment detection 期刊论文 OAI收割 IET Image Processing, 2017, 卷号: 11, 期号: 6, 页码: 416-424 作者: Zheng, Tianjiang; Chang Z(常铮); Luo HB(罗海波); Ding QH(丁庆海); Shao CY(邵春艳) \| 收藏 \| 浏览/下载：37/0 \| 提交时间：2017/07/17 image segmentation affine transforms edge detection eigenvalues and eigenfunctions line segment detection step-by-step pipeline processing approach resistant to affine transformation and monotonic intensity change descriptor RATMIC descriptor Canny detector Harris corner detector regions of interest
	Power line detection from optical images 期刊论文 OAI收割 neurocomputing, 2014, 卷号: 129, 期号: si, 页码: 350-361 作者: Song, Biqin; Li, Xuelong 收藏 \| 浏览/下载：37/0 \| 提交时间：2015/03/18 Power line detection Threat avoidance Matched filter Line segment pool Graph-cut model
	Robust Line Segment Detection Based on Non-isotropy in Low-SNR Image 会议论文 OAI收割 2012 IET International Conference on Information Science and Control Engineering (ICISCE 2012), Shenzhen, China., December 7-9, 2012 作者: Ai R(艾锐); Shi ZL(史泽林) 收藏 \| 浏览/下载：28/0 \| 提交时间：2012/12/28 line segment detection low-SNR image non-isotropy phase-grouping contrario detection framework
	Research on infrared dim-point target detection and tracking under sea-sky-line complex background (EI CONFERENCE) 会议论文 OAI收割 International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, May 24, 2011 - May 24, 2011, Beijing, China 作者: Dong Y.-X.; Zhang H.-B.; Li Y.; Li Y.; Li Y. 收藏 \| 浏览/下载：108/0 \| 提交时间：2013/03/25 Target detection and tracking technology in infrared image is an important part of modern military defense system. Infrared dim-point targets detection and recognition under complex background is a difficulty and important strategic value and challenging research topic. The main objects that carrier-borne infrared vigilance system detected are sea-skimming aircrafts and missiles. Due to the characteristics of wide field of view of vigilance system the target is usually under the sea clutter. Detection and recognition of the target will be taken great difficulties.There are some traditional point target detection algorithms such as adaptive background prediction detecting method. When background has dispersion-decreasing structure the traditional target detection algorithms would be more useful. But when the background has large gray gradient such as sea-sky-line sea waves etc.The bigger false-alarm rate will be taken in these local area.It could not obtain satisfactory results. Because dim-point target itself does not have obvious geometry or texture feature in our opinion from the perspective of mathematics the detection of dim-point targets in image is about singular function analysis.And from the perspective image processing analysis the judgment of isolated singularity in the image is key problem. The foregoing points for dim-point targets detection its essence is a separation of target and background of different singularity characteristics.The image from infrared sensor usually accompanied by different kinds of noise. These external noises could be caused by the complicated background or from the sensor itself. The noise might affect target detection and tracking. Therefore the purpose of the image preprocessing is to reduce the effects from noise also to raise the SNR of image and to increase the contrast of target and background. According to the low sea-skimming infrared flying small target characteristics the median filter is used to eliminate noise improve signal-to-noise ratio then the multi-point multi-storey vertical Sobel algorithm will be used to detect the sea-sky-line so that we can segment sea and sky in the image. Finally using centroid tracking method to capture and trace target. This method has been successfully used to trace target under the sea-sky complex background. 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).
	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：24/0 \| 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