The algorithm mainly controls the size of the smoothing area and the mean variance threshold to select the maximum correlation area containing minimum noise interference.
该算法主要通过控制平滑区域大小和均方差阈值来选择受噪声干扰最小和最大相关区域。
The threshold of each leaf is calculated through the variance of each and the estimated noise variance of the entire image.
通过每一叶节点以及整个图像的噪声的估计方差变化计算叶节点阈值。
Based on maximum between-cluster variance method and uniformity measure, this paper USES maximum entropy principle to select the gray-level threshold value for image segmentation.
在最大类间方差法和一致性准则法的基础上，运用最大熵原理来选择灰度阈值对图像进行分割。
Using the maximum between-class variance method (OTSU) obtained image binarization best treatment threshold, and thus image binarization treatment.
利用最大类间方差法（OTSU）求出对图像进行二值化处理的最佳阈值，从而进行图像二值化处理。
Then, the best threshold value is calculated basing on the gradient magnitude mean and the variance of the image.
再由图像的梯度幅值均值和方差计算出图像的最佳阈值。
This algorithm set threshold to distinguish impulse noise point, through variance rate of gray level. The information of every impulse noise point is noted in a array.
该算法通过灰度变化率设置阈值判别脉冲噪声点，并将脉冲噪声点信息记录到与图像对应的噪声记录数组中。
First, block higher order statistics and threshold via local maximal between-cluster variance are used to get the motion detection mask.
该算法首先利用分块高阶统计算法和基于最大类间方差的阈值算法得到目标的运动区域检测模板。
First, block higher order statistics and threshold via local maximal between-cluster variance are used to get the motion detection mask.
该算法首先利用分块高阶统计算法和基于最大类间方差的阈值算法得到目标的运动区域检测模板。
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