This paper presents a general data depth function with adaptive shape of deep contours based on Gaussian kernel function.
本文提出了一个具有自适应等深面的广义数据深度函数。
The background samples are chosen by thresholding inter-frame differences, and the Gaussian kernel density estimation is used to estimate the probability density function of background intensity.
通过相隔固定的帧差值阅值化得到背景样本值，并采用高斯核密度估计方法计算背景灰度的概率密度函数。
The function performs the downsampling step of the Gaussian pyramid construction. First, it convolves the source image with the kernel.
该函数执行高斯金字塔结构下采样的步骤。首先，它与内核的源图像进行卷积。
This paper based on ambiguity function theory analyzes the cause of cross-components and gives the optimization algorithm of adaptive TFD with radially-gaussian kernel.
文中以模糊函数理论为基础，分析了时频分析产生交叉项的原因，给出了以径向高斯函数为核函数的自适应时频分析优化算法。
This paper based on ambiguity function theory analyzes the cause of cross-components and gives the optimization algorithm of adaptive TFD with radially-gaussian kernel.
文中以模糊函数理论为基础，分析了时频分析产生交叉项的原因，给出了以径向高斯函数为核函数的自适应时频分析优化算法。
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