Hu矩 matlab

“Hu矩”这个词在MATLAB（一种数学软件包）中并不常见，通常我们说的是“Hurst指数”（Hurst exponent），它是一个统计学里的概念，用于描述时间序列数据的自相似性和长期记忆性。在MATLAB中，计算Hurst指数可以帮助分析信号的波动模式，判断其是否呈现出随机游走还是更像布朗运动。这个指数值通常在0到1之间，越接近1表示数据有更强的自相关性。

如果你需要在MATLAB中计算Hurst指数，可以使用`hurst exponent`函数或者编写一些自定义代码利用Box-Jenkins方法或其他相关算法。以下是基本的步骤：

% 加载时间序列数据
data = ...; % 替换为你的时间序列数据

% 计算Hurst指数
[h, p] = hurst(data);

% h就是Hurst指数，p是显著性水平

相关问题

图像块的hu矩matlab代码

以下是计算图像块的Hu矩的 Matlab 代码：

% 读取图像
img = imread('test.jpg');
% 转换为灰度图像
gray_img = rgb2gray(img);
% 提取图像块
block = gray_img(1:100, 1:100);
% 计算图像块的Hu矩
hu_moments = hu_moments(block);
disp(hu_moments);

% 计算Hu矩的函数
function hu = hu_moments(block)
    % 计算归一化的中心矩
    M00 = moment(block, 0, 0);
    M10 = moment(block, 1, 0);
    M01 = moment(block, 0, 1);
    x_bar = M10 / M00;
    y_bar = M01 / M00;
    u20 = moment(block, 2, 0) / M00 - x_bar^2;
    u02 = moment(block, 0, 2) / M00 - y_bar^2;
    u11 = moment(block, 1, 1) / M00 - x_bar * y_bar;
    u30 = moment(block, 3, 0) / M00 - 3 * x_bar * u20 + 2 * x_bar^3;
    u03 = moment(block, 0, 3) / M00 - 3 * y_bar * u02 + 2 * y_bar^3;
    u21 = moment(block, 2, 1) / M00 - 2 * x_bar * u11 - y_bar * u20 + 2 * x_bar^2 * y_bar;
    u12 = moment(block, 1, 2) / M00 - 2 * y_bar * u11 - x_bar * u02 + 2 * y_bar^2 * x_bar;
    % 计算Hu矩
    hu = zeros(1, 7);
    hu(1) = u20 + u02;
    hu(2) = (u20 - u02)^2 + 4 * u11^2;
    hu(3) = (u30 - 3 * x_bar * u20 + 2 * x_bar^3)^2 + (3 * y_bar * u02 - u03 + 2 * y_bar^3)^2;
    hu(4) = (u30 + u12)^2 + (u21 + u03)^2;
    hu(5) = (u30 - 3 * x_bar * u12 + 2 * x_bar^3) * (u30 + u12)^2 - (u03 - 3 * y_bar * u21 + 2 * y_bar^3) * (u21 + u03)^2;
    hu(6) = (u20 - u02) * (u30 + u12)^2 + (4 * u11 * (u30 + u12) * (u21 + u03)) + (u21 - u03) * (u21 + u03)^2;
    hu(7) = (3 * u21 - u03) * (u30 + u12) * ((u30 + u12)^2 - 3 * (u21 + u03)^2) - (u30 - 3 * u12) * (u21 + u03) * (3 * (u30 + u12)^2 - (u21 + u03)^2);
end

% 计算矩的函数
function m = moment(block, p, q)
    [m, n] = size(block);
    m = sum(sum(double(block) .* (repmat((0:n-1)', 1, m) .^ p) .* (repmat(0:m-1, n, 1) .^ q)));
end

该代码使用 `rgb2gray` 函数将彩色图像转换为灰度图像，然后提取图像块，最后使用 `hu_moments` 函数计算图像块的Hu矩。Hu矩是一个7维的向量，可以用来描述图像块的形状和纹理特征。

Hu矩提取matlab代码

Hu矩提取是一种基于轮廓分析的图像特征提取算法，其实现代码在Matlab中如下：

% 读取图像
I = imread('image.png');

% 将图像转化为二值图像
I_binary = imbinarize(I);

% 根据二值图像获取其轮廓
[B, L] = bwboundaries(I_binary, 'noholes');

% 计算轮廓的Hu矩
hu_moments = hu_moments(B{1});

% 输出Hu矩值
disp(hu_moments);

其中hu_moments是自定义的一个函数，实现如下：

function hu_moments = hu_moments(boundary)

% 计算轮廓的Hu矩
hu_moments = zeros(1, 7);
hu_moments(1) = inv_moment(boundary, 2, 0) + inv_moment(boundary, 0, 2);
hu_moments(2) = (inv_moment(boundary, 2, 0) - inv_moment(boundary, 0, 2))^2 + 4*inv_moment(boundary, 1, 1)^2;
hu_moments(3) = (inv_moment(boundary, 3, 0) - 3*inv_moment(boundary, 1, 2))^2 + (3*inv_moment(boundary, 2, 1) - inv_moment(boundary, 0, 3))^2;
hu_moments(4) = (inv_moment(boundary, 3, 0) + inv_moment(boundary, 1, 2))^2 + (inv_moment(boundary, 2, 1) + inv_moment(boundary, 0, 3))^2;
hu_moments(5) = (inv_moment(boundary, 3, 0) - 3*inv_moment(boundary, 1, 2))*(inv_moment(boundary, 3, 0) + inv_moment(boundary, 1, 2))*((inv_moment(boundary, 3, 0) + inv_moment(boundary, 1, 2))^2 - 3*((inv_moment(boundary, 2, 1) + inv_moment(boundary, 0, 3))^2)) + (3*inv_moment(boundary, 2, 1) - inv_moment(boundary, 0, 3))*(inv_moment(boundary, 2, 1) + inv_moment(boundary, 0, 3))*(3*(inv_moment(boundary, 3, 0) + inv_moment(boundary, 1, 2))^2 - ((inv_moment(boundary, 2, 1) + inv_moment(boundary, 0, 3))^2));
hu_moments(6) = (inv_moment(boundary, 2, 0) - inv_moment(boundary, 0, 2))*((inv_moment(boundary, 3, 0) + inv_moment(boundary, 1, 2))^2 - (inv_moment(boundary, 2, 1) + inv_moment(boundary, 0, 3))^2) + 4*inv_moment(boundary, 1, 1)*(inv_moment(boundary, 3, 0) + inv_moment(boundary, 1, 2))*(inv_moment(boundary, 2, 1) + inv_moment(boundary, 0, 3));
hu_moments(7) = (3*inv_moment(boundary, 2, 1) - inv_moment(boundary, 0, 3))*(inv_moment(boundary, 3, 0) + inv_moment(boundary, 1, 2))*((inv_moment(boundary, 3, 0) + inv_moment(boundary, 1, 2))^2 - 3*((inv_moment(boundary, 2, 1) + inv_moment(boundary, 0, 3))^2)) - (inv_moment(boundary, 3, 0) - 3*inv_moment(boundary, 1, 2))*(inv_moment(boundary, 2, 1) + inv_moment(boundary, 0, 3))*(3*(inv_moment(boundary, 3, 0) + inv_moment(boundary, 1, 2))^2 - ((inv_moment(boundary, 2, 1) + inv_moment(boundary, 0, 3))^2));

% 将Hu矩标准化
hu_moments = -sign(hu_moments).*log10(abs(hu_moments));

end

其中inv_moment也是自定义的一个函数，用来计算轮廓的非中心矩，实现如下：

function inv_m = inv_moment(boundary, p, q)

% 计算轮廓的非中心矩
inv_m = sum((boundary(:, 2).^p).*(boundary(:, 1).^q));

end

以上就是Hu矩提取的Matlab代码实现。