第十三讲-图像分割.pptVIP

Thresholding Thresholding In case of (a): light objects in dark background To extract the objects: Select a T that separates the objects from the background i.e. any (x,y) for which f(x,y)T is an object point. Multilevel Thresholding In case of (b): a more general case of this approach (multilevel thresholding) Segment image as follows: To one object class if T1f(x,y)≤T2 To the other if f(x,y)T2 To the background if f(x,y)≤T1 Thresholded Image A thresholded image is defined as: Thresholding Thresholding can be viewed as an operation that involves tests against a function T of the form: where p(x, y) denotes some local property of this point (x, y), i.e., the average level of a neighborhood centered on (x, y). If T does not depend on p(x, y) then the threshold is called global threshold, otherwise it is called local or adaptive threshold. Threshold Type When T depends only on f(x,y) ? global threshold When T depends on both f(x,y) and p(x,y) ? local threshold When T depends on x and y (in addition) ? dynamic threshold Basic Global Thresholding Assume that the background and the object occupy comparable areas in the image, a good initial value of T is the average gray level of the image. 1. Select an initial estimate for T. 2. Segment the image into two group of pixels G1 and G2 using T. 3. Compute the average gray level values of G1 and G2 are μ1 and μ2. 4. Compute a new threshold value as Tnew=(μ1+μ2)/2, 5. Compare if |Tnew-T|z0 ( predefined thredhold z0) then T=Tnew and go to step 2, else stop 用下面的算法(P599)可以自动的得到阈值T： 选择一个T的初始估计值 用阈值T分割图像，这样会生成两组像素：G1为所有灰度值大于T的像素组成，G2由所有灰度值小于T的像素组成。 分别计算区域G1、 G2所有像素的平均灰度值μ1、μ2 计算新的阈值： 重复2到4，直到T值之差小于事先定义的参数T0 全局阈值分割 T0=0 T=125.4 3. 最小误差的方法 若以阈值t进行分割时，把目标像素错分为背景像素的概率为 把背景像素错分为目标像素的概率为 总的错误概率为 为使这个误差最小 因为假设背景与前景的灰度分布都是正态分布的 带入可得 二、局部阈值方法 若分割过程中对图像上每个像素所使用的阈值不相等，则为局部阈值方法。通常用于照度不均匀或灰度连续变化的图像的分割。 三、动态阈值方法 当照明不均匀、有突发噪声或者背景变化比较大的时候，整幅图像将没有一个合适的单一阈值。这时可以对图像进行分块处理，进一步细分为子图像，对每子图像分别选定一个阈值进行分割。这种方法的关键问题是如何将图像进行细分和如何为得到的子