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基于Mean Shift聚类的边缘检测方法 被引量:8

Edge Detection Method Based on Mean Shift Clustering Method
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摘要 噪声图像中的边缘检测是图像处理中一项很困难的任务。基于Mean Shift的图像平滑能够有效地去除图像中的高斯噪声,同时保持图像的边缘,然后通过ADM掩模算法检测边缘。经过实验证明,能够很好地提取图像的边缘,与其他经典方法相比具有比较好的效果。 Edge detection in noise image is a hard task of image processing. Noise can be removed by smoothing image with mean shift method and at the same time. details of edge in image can be remained. And then edge is detected by absolute difference mask(ADM) method. Experiments show this approach can get better results than other ordinary methods.
出处 《弹箭与制导学报》 CSCD 北大核心 2007年第1期366-368,共3页 Journal of Projectiles,Rockets,Missiles and Guidance
基金 武器装备预研基金资助
关键词 边缘检测 Mean SHIFT 核密度估计 ADM edge detection Mean Shirt kernel density estimation ADM
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参考文献7

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同被引文献51

  • 1连洁,韩传久.基于Mean Shift的红外目标自动跟踪方法[J].微计算机信息,2008,24(4):279-281. 被引量:3
  • 2陈卓夷.基于非参数密度估计聚类的关键帧提取方法[J].计算机科学,2007,34(4):119-120. 被引量:8
  • 3魏军伟,方敏.基于最大熵和形态学的边缘检测[J].计算机工程与应用,2007,43(21):70-71. 被引量:11
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  • 9Dosselmann R,Xue Dong Yang.Mean shift point-mass level-of- detail[C].Canadian Conference on Electrical and Computer Engineering,2008:37-42.
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