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用傅里叶相移特性估计位移 总被引:1,自引:0,他引:1
提出了一种从频率域出发,估计出运动物体在空间域位移的算法。利用傅里叶变换的自配准性质和位移特性,用极坐标形式下连续图像相位谱的差直接估计运动目标的位移。与传统的寻找迪拉克峰值的方法相比,本方法无需重新变换回空间域,从而节省了处理时间,具有更好的实时性。实验证明,这种先求出相位谱的差,再用其周期数来估计位移的算法简单明了,其分辨能力不低于一个像素。该理论可以应用于图像跟踪和图像后期处理中。 相似文献
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基于理性分析,提出了一种演化相位谱模型,并由此发展了脉动风速模拟方法。根据湍流中不同频率涡旋的特征速度,提出了相位演化速度这一概念;进而,说明具体的风速时程可由所有初始相位为零的涡旋经过时间 演化而来。通过对实测脉动风速 值的识别和统计,给出了 的概率分布。据此,可以得到演化相位谱的样本,结合Fourier幅值谱,应用逆Fourier变换便可进行脉动风速模拟。本文所建立的演化相位谱模型是对Fourier相位谱的一种理性描述,可用于各种结构抗风计算及可靠度分析的风荷载模拟当中。 相似文献
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The objective of our study is to develop a theoretical framework for conditional random fields (CRFs) which consist on non-stationary stochastic processes, using probability density functions of Fourier amplitudes and phases in the frequency domain. The problem area of CRFs in this study is limited to the estimation of stochastic processes conditioned by realized values of time series at one site.
To represent the properties of non-stationary processes, we will introduce group delay time spectra, which are gradient of phase spectra with respect to frequency. Using the style of likelihood method, the conditional probability density functions of Fourier phases are updated by information of group delay time. Then, a method to generate numerically the conditional random fields containing non-stationary processes is developed and it is verified through the numerical examples that the method can give reasonable results. 相似文献
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