Because of wide variation in gray levels and particle dimensions and the presence of many small gravel objects in the background, as well as corrupting the image by noise, it is difficult to segment gravel objects. In...Because of wide variation in gray levels and particle dimensions and the presence of many small gravel objects in the background, as well as corrupting the image by noise, it is difficult to segment gravel objects. In this paper, we develop a partial entropy method and succeed to realize gravel objects segmentation. We give entropy principles and fast calculation methods. Moreover, we use minimum entropy error automaticly to select a threshold to segment image. We introduce the filter method using mathematical morphology. The segment experiments are performed by using different window dimensions for a group of gravel image and demonstrates that this method has high segmentation rate and low noise sensitivity.展开更多
On-line partial discharge(PD)detection still remains a very challenging task because of the strong electromagnetic interferences.In this paper,a new method of de-noising,using complex Daubechies wavelet(CDW)transform,...On-line partial discharge(PD)detection still remains a very challenging task because of the strong electromagnetic interferences.In this paper,a new method of de-noising,using complex Daubechies wavelet(CDW)transform,has been proposed.It is a relatively recent enhancement to the real-valued wavelet transform because of tow important properties,which are nearly shift-invariant and availability of phase information.Those properties give CDW transform superiority over other real-valued wavelet transform,and then the construction algorithm of CDW is introduced in detail.Secondly,based on the real threshold algorithm of real-valued wavelet transform,complex threshold algorithm is devised.This algorithm take the different characteristics of real part and imaginary part of complex wavelet coefficients into account,it modifies the real and imaginary parts of complex wavelet coefficients respectively.Thirdly,to obtain a real de-noised signal,new combined information series is devised.By applying different combination of real part and imaginary part of de-noised complex signal,a real de-noised signal can be restored with higher peak signal-to-noise ratio(PSNR)and less distortion of original signals.Finally,On-site applications of extracting PD signals from noisy background by the optimal de-noising scheme based on CDW are illustrated.The on-site experimental results show that the optimal de-noising scheme is an effective way to suppress white noise in PD measurement.展开更多
基金the National Natural Science Foundationof China!(No.49874027)
文摘Because of wide variation in gray levels and particle dimensions and the presence of many small gravel objects in the background, as well as corrupting the image by noise, it is difficult to segment gravel objects. In this paper, we develop a partial entropy method and succeed to realize gravel objects segmentation. We give entropy principles and fast calculation methods. Moreover, we use minimum entropy error automaticly to select a threshold to segment image. We introduce the filter method using mathematical morphology. The segment experiments are performed by using different window dimensions for a group of gravel image and demonstrates that this method has high segmentation rate and low noise sensitivity.
基金Project Supported by National Natural Science Foundation China（50577069）, National Grid Company （2004-SGKJ）.
文摘On-line partial discharge(PD)detection still remains a very challenging task because of the strong electromagnetic interferences.In this paper,a new method of de-noising,using complex Daubechies wavelet(CDW)transform,has been proposed.It is a relatively recent enhancement to the real-valued wavelet transform because of tow important properties,which are nearly shift-invariant and availability of phase information.Those properties give CDW transform superiority over other real-valued wavelet transform,and then the construction algorithm of CDW is introduced in detail.Secondly,based on the real threshold algorithm of real-valued wavelet transform,complex threshold algorithm is devised.This algorithm take the different characteristics of real part and imaginary part of complex wavelet coefficients into account,it modifies the real and imaginary parts of complex wavelet coefficients respectively.Thirdly,to obtain a real de-noised signal,new combined information series is devised.By applying different combination of real part and imaginary part of de-noised complex signal,a real de-noised signal can be restored with higher peak signal-to-noise ratio(PSNR)and less distortion of original signals.Finally,On-site applications of extracting PD signals from noisy background by the optimal de-noising scheme based on CDW are illustrated.The on-site experimental results show that the optimal de-noising scheme is an effective way to suppress white noise in PD measurement.
