kernel density function

基本解釋

Using this method, kernel function could be flexibly chosen to estimate sample point's density values according to different locating application scenes.
推廣后的定位方法，可根據(jù)具體的目標(biāo)定位場合，靈活選擇核函數(shù)對(duì)樣本點(diǎn)進(jìn)行核密度估計(jì)。
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(hào)的概率密度函數(shù)，并由此獲得關(guān)鍵的核函數(shù)，進(jìn)而從混合信號(hào)中分離出源信號(hào)。
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(jì)方法計(jì)算背景灰度的概率密度函數(shù)。