MATLAB 亚像素边缘检测：subpixel_edge_position 函数实现

% 读取图像 img = imread('lena.jpg'); % 将图像转为灰度图 gray_img = rgb2gray(img); % 使用Canny算法进行边缘检测 canny_img = edge(gray_img, 'canny'); % 设置窗口大小 window_size = 5; % 计算图像梯度 [dx, dy] = gradient(double(gray_img)); gradient_magnitude = sqrt(dx.^2 + dy.^2); % 初始化亚像素边缘坐标 subpixel_edges = zeros(size(gray_img)); % 对每个边缘像素进行精确定位 for i = 1:size(canny_img, 1) for j = 1:size(canny_img, 2) if canny_img(i, j) == 1 % 获取窗口内的梯度幅值 window_gradient = gradient_magnitude(max(i-window_size, 1):min(i+window_size, end), max(j-window_size, 1):min(j+window_size, end)); % 计算窗口内梯度幅值的加权均值 weights = fspecial('gaussian', size(window_gradient), 2); weighted_gradient = window_gradient .* weights; weighted_sum = sum(weighted_gradient(:)); weighted_mean = weighted_sum / sum(weights(:)); % 通过高斯曲线拟合计算亚像素边缘位置 subpixel_edges(i, j) = subpixel_edge_position(window_gradient, weighted_mean); end end end % 显示亚像素边缘检测结果 subplot(121); imshow(canny_img); subplot(122); imshow(subpixel_edges);

% subpixel_edge_position 函数的编写如下：

function subpixel_position = subpixel_edge_position(window_gradient, weighted_mean) % 计算窗口内梯度幅值的位置 [~, max_index] = max(window_gradient(:)); [row, col] = ind2sub(size(window_gradient), max_index);

% 计算亚像素边缘位置
if row == 1 || row == size(window_gradient, 1)
    subpixel_position = row;
else
    % 计算亚像素边缘位置的偏移量
    offset = (window_gradient(row+1, col) - window_gradient(row-1, col)) / (2 * (window_gradient(row+1, col) - 2 * window_gradient(row, col) + window_gradient(row-1, col)));
    subpixel_position = row + offset;
end

% 将亚像素边缘位置映射到原图像坐标
subpixel_position = subpixel_position - (size(window_gradient, 1) - 1) / 2 + weighted_mean;

end