资源简介
matlab程序用了三种方法分别对图像进行二值化
代码片段和文件信息
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 kittler‘s
% 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
var2 = (((i+1 : n)‘-miu2).^2)‘ * prob(i+2 : n+1);
var2 = var2 / (1-w0);
if var1 > eps && var2 > eps % in case of var1=0 or var2 =0
J_t(i+1) = 1+w0 * log(var1)+(1-w0) * log(var2)-2*w0*log(w0)-2*(1-w0)*log(1-w0);
else
J_t(i+1) = MAXD;
end
end
end
minJ = min(J_t);
index = find(J_t == minJ);
th = mean(index);
th = (th-1)/n
imagBW = im2bw(imag th);
% figure imshow(imagBW) title(‘kittler binary‘);
属性 大小 日期 时间 名称
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文件 1845 2010-05-04 16:48 二值化\kittlerMet.m
文件 423 2010-05-05 14:18 二值化\main.m
文件 2606 2010-05-04 16:48 二值化\niblack.m
文件 1642 2010-05-04 16:48 二值化\otsu.m
目录 0 2010-05-13 14:50 二值化
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