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Certainly! Below is a simple MATLAB implementation of the Split and Merge algorithm for image segmentation:

function segmented_image = split_and_merge(image, threshold)
    % Convert image to grayscale if it's not already
    if size(image, 3) == 3
        image = rgb2gray(image);
    end
    segmented_image = split(image, threshold);
end

function region = split(image, threshold)
    % Get size of the image
    [rows, cols] = size(image);
    % Base case: if the region is small enough, return it
    if (rows <= 1) || (cols <= 1)
        region = image;
        return;
    end
    % Calculate mean and variance
    mean_val = mean(image(:));
    var_val = var(double(image(:)));
    
    % If variance is less than the threshold, merge the region
    if var_val < threshold
        region = mean_val * ones(size(image));
    else
        % Otherwise, split the region into quadrants
        mid_row = floor(rows / 2);
        mid_col = floor(cols / 2);
        region = [split(image(1:mid_row, 1:mid_col), threshold), split(image(1:mid_row, mid_col+1:end), threshold);
                  split(image(mid_row+1:end, 1:mid_col), threshold), split(image(mid_row+1:end, mid_col+1:end), threshold)];
    end
end

This code defines a simple split-and-merge algorithm that segments an image based on a specified variance threshold. Note that this is a basic implementation and may require further refinements for practical applications.

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1mo ago

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