To overcome the shortcoming that the traditional minimum error threshold method can obtain satisfactory image segmentation results only when the object and background of the image strictly obey a certain type of proba...To overcome the shortcoming that the traditional minimum error threshold method can obtain satisfactory image segmentation results only when the object and background of the image strictly obey a certain type of probability distribution,one proposes the regularized minimum error threshold method and treats the traditional minimum error threshold method as its special case.Then one constructs the discrete probability distribution by using the separation between segmentation threshold and the average gray-scale values of the object and background of the image so as to compute the information energy of the probability distribution.The impact of the regularized parameter selection on the optimal segmentation threshold of the regularized minimum error threshold method is investigated.To verify the effectiveness of the proposed regularized minimum error threshold method,one selects typical grey-scale images and performs segmentation tests.The segmentation results obtained by the regularized minimum error threshold method are compared with those obtained with the traditional minimum error threshold method.The segmentation results and their analysis show that the regularized minimum error threshold method is feasible and produces more satisfactory segmentation results than the minimum error threshold method.It does not exert much impact on object acquisition in case of the addition of a certain noise to an image.Therefore,the method can meet the requirements for extracting a real object in the noisy environment.展开更多
This paper presents a minimum error thresholding (MET) algorithm under the hypothesis that the gray level histogram of SAR image fits to a mixture model of shifted Rayleigh distribution. This algorithm is applied to r...This paper presents a minimum error thresholding (MET) algorithm under the hypothesis that the gray level histogram of SAR image fits to a mixture model of shifted Rayleigh distribution. This algorithm is applied to real SAR images and compared with traditional Otsu algorithm and other MET algorithms based on various models of histogram. The hypothesis of using Rayleigh distribution model is confirmed by Kolmogorov-Smirnov testing and the comparison results obtained show that the proposed new algorithm has good performance in thresholding SAR images.展开更多
针对现有混沌检测算法精度不高、状态响应滞后的问题,该文从混沌状态整体性、系统解频域特性等角度进行全面分析,提出一种基于摄动解主频功率比的弱信号检测方法,该算法不仅准确实现了临界状态的有效界定,提高了信号检测的可靠程度,而...针对现有混沌检测算法精度不高、状态响应滞后的问题,该文从混沌状态整体性、系统解频域特性等角度进行全面分析,提出一种基于摄动解主频功率比的弱信号检测方法,该算法不仅准确实现了临界状态的有效界定,提高了信号检测的可靠程度,而且揭示了系统各个状态之间的差别及物理含义。文中采用参数摄动法推导了Duffing-Van der pol振子的一阶摄动平衡解,证明了其为影响主频率分量的主要因素。在此基础上,采用经验模态分解方法对有效参量信息进行选择性重构,以最小均方误差约束准则下的比值系数重新定义了系统状态,得到系统主频功率比与策动力幅值之间的映射关系,并以此作为临界阈值确定的依据。实验结果表明,采用主频功率比准则的信号检测方法可靠性提高了约1个数量级,且算法的响应速度为传统分析方法的2倍以上。展开更多
基金supported by the National Natural Science Foundations of China(Nos.61136002,61472324)the Natural Science Foundation of Shanxi Province(No.2014JM8331)
文摘To overcome the shortcoming that the traditional minimum error threshold method can obtain satisfactory image segmentation results only when the object and background of the image strictly obey a certain type of probability distribution,one proposes the regularized minimum error threshold method and treats the traditional minimum error threshold method as its special case.Then one constructs the discrete probability distribution by using the separation between segmentation threshold and the average gray-scale values of the object and background of the image so as to compute the information energy of the probability distribution.The impact of the regularized parameter selection on the optimal segmentation threshold of the regularized minimum error threshold method is investigated.To verify the effectiveness of the proposed regularized minimum error threshold method,one selects typical grey-scale images and performs segmentation tests.The segmentation results obtained by the regularized minimum error threshold method are compared with those obtained with the traditional minimum error threshold method.The segmentation results and their analysis show that the regularized minimum error threshold method is feasible and produces more satisfactory segmentation results than the minimum error threshold method.It does not exert much impact on object acquisition in case of the addition of a certain noise to an image.Therefore,the method can meet the requirements for extracting a real object in the noisy environment.
基金Supported by the National Natural Foundation of China(No.69672029 and No.69772021)
文摘This paper presents a minimum error thresholding (MET) algorithm under the hypothesis that the gray level histogram of SAR image fits to a mixture model of shifted Rayleigh distribution. This algorithm is applied to real SAR images and compared with traditional Otsu algorithm and other MET algorithms based on various models of histogram. The hypothesis of using Rayleigh distribution model is confirmed by Kolmogorov-Smirnov testing and the comparison results obtained show that the proposed new algorithm has good performance in thresholding SAR images.
文摘针对现有混沌检测算法精度不高、状态响应滞后的问题,该文从混沌状态整体性、系统解频域特性等角度进行全面分析,提出一种基于摄动解主频功率比的弱信号检测方法,该算法不仅准确实现了临界状态的有效界定,提高了信号检测的可靠程度,而且揭示了系统各个状态之间的差别及物理含义。文中采用参数摄动法推导了Duffing-Van der pol振子的一阶摄动平衡解,证明了其为影响主频率分量的主要因素。在此基础上,采用经验模态分解方法对有效参量信息进行选择性重构,以最小均方误差约束准则下的比值系数重新定义了系统状态,得到系统主频功率比与策动力幅值之间的映射关系,并以此作为临界阈值确定的依据。实验结果表明,采用主频功率比准则的信号检测方法可靠性提高了约1个数量级,且算法的响应速度为传统分析方法的2倍以上。
