超空泡图像的自适应多尺度小波边缘检测

Abstract

Abstract:

Aiming at the undesirable noise in the image of Super Vacuoles image,which is acquired from the high-speed projectiles test underwater,and the poor ability of traditional edge detection algorithm to restrain the noise in auto edge detection,this paper uses the self-adaptive multi-scale wavelet edge detection to detect the edge of Super Vacuoles image.First,we carry out the multi-scale wavelet transform and the enhancement of neighboring scale gradient.Then,we use the K-Means clustering to auto detection the edge,after which we obtain the edge of different scale.Finally,we choose the Multi-scale fusion strategy according to the wavelet and fuse the multi-scales edge into the final edge.The Experiments show that compared with the traditional edge detection algorithm,the proposed algorithm suppresses the edge noise more effectively,and obtains more information of the edge.

Key words: super vacuoles image;adaptive multi-scale wavelet edge detection algorithm;k-means clustering;multi-scale fusion

CLC Number:

TN911.73

LEI Bin, HOU Shuai-Ge, SHEN Yan-Hui. Adaptive Multi-scale Wavelet Edge Detection of Super Vacuoles Image[J]., 2011, 24(9): 104-.

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Adaptive Multi-scale Wavelet Edge Detection of Super Vacuoles Image

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