请用MATLAB写一个meanshift图像分割的算法

当然，我可以用 MATLAB 写一个 meanshift 图像分割算法。以下是代码示例：

function segImage = meanshiftSegmentation(image)
% image: 原始图像
% segImage: 分割后的图像

% 加载工具箱
if isempty(ver('image'))
    error('MATLAB Image Processing Toolbox is required.');
end

% 初始化参数
spatialRadius = 10;   % 空间窗口半径
colorRadius = 10;     % 颜色窗口半径
maxLevel = 10;        % 最大迭代次数

% 图像预处理
image = im2double(image);

% meanshift 分割
[rows, cols, ~] = size(image);
for i = 1:maxLevel
    % 计算每个像素点的密度估计
    densityEstimation = zeros(rows, cols, 3);
    for j = 1:3
        densityEstimation(:,:,j) = densityEstimation(:,:,j) + ...
            imfilter(image(:,:,j), fspecial('gaussian', [spatialRadius spatialRadius], ...
            sqrt(2)*spatialRadius/3), 'same', 'conv', 'replicate');
        densityEstimation(:,:,j) = densityEstimation(:,:,j) .* ...
            imfilter(image(:,:,j), fspecial('gaussian', [colorRadius colorRadius], ...
            sqrt(2)*colorRadius/3), 'same', 'conv', 'replicate');
    end
    
    % 计算移动向量
    [X, Y] = meshgrid(1:cols, 1:rows);
    X = X(:);
    Y = Y(:);
    for j = 1:3
        FlattenedDensityEstimation = reshape(densityEstimation(:,:,j), [], 1);
        FlattenedDensityEstimation = FlattenedDensityEstimation/sum(FlattenedDensityEstimation);
        Xj = X .* FlattenedDensityEstimation;
        Yj = Y .* FlattenedDensityEstimation;
        Xj = sum(Xj);
        Yj = sum(Yj);
        meanPoint = [Xj, Yj] ./ sum(FlattenedDensityEstimation);
        
        % 更新像素位置
        distance = sqrt((X-meanPoint(1)).^2 + (Y-meanPoint(2)).^2);
        isInsideWindow = distance <= sqrt(spatialRadius^2 + colorRadius^2);
        image(:,:,j) = sum(densityEstimation(:,:,j) .* isInsideWindow, 3) ./ ...
            sum(isInsideWindow, 3);
    end
end

% 反归一化，输出分割图像
segImage = image;
segImage = uint8(segImage * 255);

end

以上是一个简单的 meanshift 图像分割算法，可以将图像分割成不同的区域。这个算法可以用于图像处理、计算机视觉、机器学习等领域。