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清华大学 [2]
厦门大学 [1]
长春光学精密机械与物... [1]
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期刊论文 [3]
会议论文 [1]
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2012 [1]
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一类适用于部分响应信道的原模图LDPC码
期刊论文
2012
陈平平
;
方毅
;
王琳
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  |  
浏览/下载:3/0
  |  
提交时间:2016/05/17
原模图低密度奇偶校验码
误比特率
错误地板
近似规则
protograph low density parity check codes
bit error rate(BER)
error floor
near-regular
基于半规则纹理合成的流场可视化技术
期刊论文
2010, 2010
王斌
;
王文平
;
雍俊海
;
孙家广
;
Wang Bin
;
Wang Wenping
;
Yong Junhai
;
Sun Jiaguang
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  |  
浏览/下载:3/0
Flow visualization by near-regular texture synthesis
期刊论文
2010, 2010
Wang Bin
;
Wang Wenping
;
Yong Junhai
;
Sun Jiaguang
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浏览/下载:2/0
A segment detection method based on improved Hough transform (EI CONFERENCE)
会议论文
ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China
Han Q.-L.
;
Zhu M.
;
Yao Z.-J.
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浏览/下载:19/0
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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
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