A new fast algorithm was presented to accelerate the computation of mutual information of images based on kernel density estimate.
针对基于核密度估计的图像互信息估计法运算量很大的问题，提出了一种快速互信息估计算法。
A new background model of non-parameter kernel density estimate was presented on the basis of abundant study on algorithms of moving object detection.
在充分研究现有运动目标检测算法的基础上，提出了一种新的非参数核密度估计背景模型。
Using this method, kernel function could be flexibly chosen to estimate sample point's density values according to different locating application scenes.
推广后的定位方法，可根据具体的目标定位场合，灵活选择核函数对样本点进行核密度估计。
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 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。
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