灰度阀值变换及二值化.docxVIP

当图像的像素点的灰度大于T的时候，设置这个点为全黑，要不然为全白。这样可以只选择我们感兴趣的领域。 im2bw(I,level); %阈值法从灰度图、RGB图创建二值图。level为人工设定阈值(threshold value)，范围为[0 ,1] 最大类间方差法（OTSU算法） 最大类间方差法是由日本学者大津(Nobuyuki Otsu)于1979年提出的,是一种自适应的阈值确定的方法,又叫大律法,简称OTSU。它是按图像的灰度特性,将图像分成背景和目标2部分。背景和目标之间的类间方差越大,说明构成图像的2部分的差别越大,当部分目标错分为背景或部分背景错分为目标都会导致2部分差别变小。因此,使类间方差最大的分割意味着错分概率最小。 在Matlab中，?graythresh函数使用最大类间方差法获得图像的阈值。 (注意标点‘‘要换一下) I = imread(‘beauty_yellowflowers.jpg’);thresh= graythresh(I);%自适应设置阀值bw1 = im2bw(I, thresh);bw2 = im2bw(I, 130/255);%手工设置阀值subplot(1,3,1);imshow(I);title(‘original’)subplot(1,3,2);imshow(bw1);title(‘autoset_thresh’);subplot(1,3,3);imshow(bw2);?title(‘thresh=130’); 最小分类错误全局二值化算法 (kittlerMet算法) 函数源代码： function imagBW = kittlerMet(imag) % KITTLERMET binarizes a gray scale image imag into a binary image % Input: %?? imag: the gray scale image, with black foreground(0), and white %?? background(255). % Output: %?? imagBW: the binary image of the gray scale image imag, with kittlers %?? minimum error thresholding algorithm. ? % Reference: %?? J. Kittler and J. Illingworth. Minimum Error Thresholding. Pattern %?? Recognition. 1986. 19(1):41-47 ? MAXD = 100000; imag = imag(:,:,1); [counts, x] = imhist(imag);? % counts are the histogram. x is the intensity level. GradeI = length(x);?? % the resolusion of the intensity. i.e. 256 for uint8. J_t = zeros(GradeI, 1);? % criterion function prob = counts ./ sum(counts);? % Probability distribution meanT = x * prob;? % Total mean level of the picture % Initialization w0 = prob(1);?? % Probability of the first class miuK = 0;?? % First-order cumulative moments of the histogram up to the kth level. J_t(1) = MAXD; n = GradeI-1; for i = 1 : n ??? w0 = w0 + prob(i+1); ??? miuK = miuK + i * prob(i+1);? % first-order cumulative moment ??? if (w0 eps) || (w0 1-eps) ??????? J_t(i+1) = MAXD;??? % T = i ??? else ??????? miu1 = miuK / w0; ??????? miu2 = (meanT-miuK) / (1-w0); ??????? var1 = (((0 : i)-miu1).^2) * prob(1 : i+1); ??????? var1 = var1 / w0;? % variance ????