灰度阀值变换及二值化

0x Tf x 255x T

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 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');

MATLAB程序：

I = imread('beauty_yellowflowers.jpg');

imagSW = kittlerMet(I);%Kittler 算法

bw1 = im2bw(I, 130/255);%手工设置阀值

subplot(1,3,1);imshow(I);title('original');

subplot(1,3,2);imshow(imagSW);title('kittler binary');

subplot(1,3,3);imshow(bw1); title('thresh=130');

结果：

Niblack二值化算法：

Niblack二值化算法是比较简单的局部阈值方法，阈值的计算公式是T = m + k*v，其中m为以该像素点为中心的区域的平均灰度值，v是该区域的标准差，k是一个系数。matlab程序如下：

I = imread(' beauty_yellowflowers.jpg ');

I = rgb2gray(I);

w = 2;%

max = 0;

min = 0;

[m,n] = size(I);

T = zeros(m ,n );

%

for i = (w + 1):(m - w)

for j = (w + 1):(n - w)

sum = 0;

for k = -w:w

for l = -w:w

sum = sum + uint32(I(i + k,j + l));

end

end

average = double(sum) /((2*w+1)*(2*w+1));

s = 0;

for k = -w:w

for l = -w:w

s = s + (uint32(I(i + k,j average)*(uint32(I(i + k,j + l)) - average);

end

end

s= sqrt(double(s)/((2*w+1)*(2*w+1))); + l)) -

T(i,j) = average + 0.2*s;

end

end

for i = 1:m

for j = 1:n

if I(i,j) > T(i,j)

I(i,j) = uint8(255);

else

I(i,j) = uint8(0);

end

end

end

imshow(I);

此种算法速度很慢，一直都没等到结果，也有可能是程序中有死循环，，费解

改进的算法如下：（也挺费时间的，效果不好）

I = imread(' beauty_yellowflowers.jpg ');

I = rgb2gray(I);

[m,n] = size(I);

block = 10;

ver = floor(m/block);

hor = floor(n/block);

T = zeros(m,n);

for b_ver = 1:block

for b_hor = 1: block

% T((ver * (b_ver - 1)+1) : (ver *b_ver),(hor *(b_hor - 1) +

1):(hor*b_hor)) = otsu(I((ver * (b_ver - 1)+1) : (ver *b_ver),(hor *(b_hor - 1) + 1):(hor*b_hor)));

t = 0;

for i = (ver * (b_ver - 1)+1) : (ver * b_ver)

for j = (hor * (b_hor - 1) + 1):(hor * b_hor) t = t + uint32(I(i,j));

end

end

t = double(t)/(ver * hor);

std_deviation = 0;

for i = (ver * (b_ver - 1)+1) : (ver * b_ver)

for j = (hor * (b_hor - 1) + 1):(hor * b_hor)

std_deviation = std_deviation + (uint32(I(i,j)) - t)*(uint32(I(i,j)) - t);

end

end

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