最大类间方差法是一种常用而有效的图像分割算法，并已在许多实时系统中采用。
Otsu method is a common and efficient image segmentation algorithm, and has been used in many various real-time systems.
最大类间方差法是一种常用而有效的图像分割算法，并已在许多实时场合中采用。
The Otsu's method is an effective algorithm for image segmentation, and has been widely employed in various real-time applications.
对最大类间方差法的改进主要表现在两个方面：建立阈值的评价准则和提高算法的运行速度。
The improvement in Ostu algorithm is represented in two aspects: establishing evaluation rules of segmentation threshold and accelerating the algorithm's operation.
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。
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.
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。
Using the maximum between-class variance method (OTSU) obtained image binarization best treatment threshold, and thus image binarization treatment.
在图像中利用粗糙集理论对图像特征数据进行有效约简，并和最大类间方差法结合，将图像分为目标和背景两部分。
Based on the Rough Set Theory, the image data is simplified and classified into targets and backgrounds with the combination of the OTSU method.
最大类间方差法是一种常用而有效的图像分割算法，但由于计算量较大，不适宜于红外成像跟踪系统中高帧频的实时图像处理。
The Otsu's method is an effective algorithm for image segmentation. But it is unfit for the real-time image processing in the high frame rate infrared imaging tracking system for it is time-consuming.
在对采集的实时序列图像进行滤波处理后，本文通过改进最大类间方差法，并将其和自适应波门跟踪方法结合起来实现运动目标的跟踪。
After preprocessing the sampled real-time image sequences, moving target tracking is realized by combining improved Ostu algorithm and adaptive window tracing method.
最大类间方差法的二值化算法是基于类间方差达到最大为准则，采用该法获得了理想的二值化效果，较准确地从岩心剖面中提取出裂缝图像信息。
The optimal effect of binarization arithmetics is the maximum variance between various classifications. Core fissure image is acquired from core splitting surface images by the use of this method.
针对图像背景噪声强的特点，提出了一种基于感兴趣区域(ROI)的结构光条纹中心处理方法，即在ROI内用最大类间方差法进行阈值分割再用重心法进行细化。
Aim at removing the background noise in the large measuring range system (about 2m), a method which combines Otsu based on ROI and gray barycenter method is proposed.
针对图像背景噪声强的特点，提出了一种基于感兴趣区域(ROI)的结构光条纹中心处理方法，即在ROI内用最大类间方差法进行阈值分割再用重心法进行细化。
Aim at removing the background noise in the large measuring range system (about 2m), a method which combines Otsu based on ROI and gray barycenter method is proposed.
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