1 of 1
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![2.1.2 迭代法
迭代式阈值选取的基本思路是:首先根据图像中物体的灰度分布情况，选取一个
近似阈值作为初始阈值，一个较好的方法就是将图像的灰度均值作为初始阈值；然
[5]
后通过分割图像和修改阈值的迭代过程获得认可的最佳阈值 。迭代式阈值选取过
程可描述如下。
(1)选取一个初始阈值T。
(2)利用阈值T把给定图像分割成两组图像，记为R1,R2。
(3)计算R1和R2均值 u1和 u2 。
(4)选取新的阈值T,且
T=（u1+u2）/2 ??（2-4 )
(5)重复第(2)~(4)步，直至R1和R2均值u1和u2,不再变化为止。
具体实现时，首先根据初始开关函数将输入图逐个图像分为前景和背景，在第一遍
对图像扫描结束后，平均两个积分器的值以确定一个阈值。用这个阈值控制开关再
次将输入图分为前景和背景，并用做新的开关函数。如此反复迭带直到开关函数不
在发生变化，此时得到的前景和背景即为最终分割结果。迭代所得的阈值分割的图
像效果良好。基于迭代的阈值能区分出图像的前景和背景的主要区域所在，但在图
像的细微处还没有很好的区分度。对某些特定图像，微小数据的变化却会引起分割
效果的巨大改变，两者的数据只是稍有变化，但分割效果却反差极大。对于直方图
双峰明显，谷底较深的图像，迭代方法可以较快地获得满意结果，但是对于直方图
双峰不明显，或图像目标和背景比例差异悬殊，迭代法所选取的阈值不如其它方法。](https://image.slidesharecdn.com/random-121023103911-phpapp02/85/-1-320.jpg)
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See the original article on Forbes here:
http://www.forbes.com/forbes/welcome/?toURL=http://www.forbes.com/sites/carminegallo/2016/05/06/5-public-speaking-tips-ted-gives-its-speakers/&refURL=&referrer=#5c07a8221d9bChatGPT and the Future of Work - Clark Boyd
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- 1. 2.1.2 迭代法 迭代式阈值选取的基本思路是:首先根据图像中物体的灰度分布情况，选取一个 近似阈值作为初始阈值，一个较好的方法就是将图像的灰度均值作为初始阈值；然 [5] 后通过分割图像和修改阈值的迭代过程获得认可的最佳阈值 。迭代式阈值选取过 程可描述如下。 (1)选取一个初始阈值T。 (2)利用阈值T把给定图像分割成两组图像，记为R1,R2。 (3)计算R1和R2均值 u1和 u2 。 (4)选取新的阈值T,且 T=（u1+u2）/2 ??（2-4 ) (5)重复第(2)~(4)步，直至R1和R2均值u1和u2,不再变化为止。 具体实现时，首先根据初始开关函数将输入图逐个图像分为前景和背景，在第一遍 对图像扫描结束后，平均两个积分器的值以确定一个阈值。用这个阈值控制开关再 次将输入图分为前景和背景，并用做新的开关函数。如此反复迭带直到开关函数不 在发生变化，此时得到的前景和背景即为最终分割结果。迭代所得的阈值分割的图 像效果良好。基于迭代的阈值能区分出图像的前景和背景的主要区域所在，但在图 像的细微处还没有很好的区分度。对某些特定图像，微小数据的变化却会引起分割 效果的巨大改变，两者的数据只是稍有变化，但分割效果却反差极大。对于直方图 双峰明显，谷底较深的图像，迭代方法可以较快地获得满意结果，但是对于直方图 双峰不明显，或图像目标和背景比例差异悬殊，迭代法所选取的阈值不如其它方法。