gaussian probability-density function

基本解釋

By the general assumption that the probability density function of the kernel function can be isolated from separate sub-Gaussian signal or super-signals.
盲源分離算法通常需預(yù)先假設(shè)源信號的概率密度函數(shù)，并由此獲得關(guān)鍵的核函數(shù)，進而從混合信號中分離出源信號。
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.
通過相隔固定的幀差值閱值化得到背景樣本值，并采用高斯核密度估計方法計算背景灰度的概率密度函數(shù)。