数字图像处理车牌识别matlab

数字图像处理车牌识别是一种常见的应用，Matlab提供了很多工具和函数来实现这个任务。以下是一个简单的车牌识别的流程：

1. 读取图像并进行预处理，例如灰度化、二值化、去噪等操作。

img = imread('car.jpg');
gray_img = rgb2gray(img);
binary_img = imbinarize(gray_img);
denoised_img = medfilt2(binary_img, [3, 3]);

2. 对图像进行形态学处理，例如膨胀、腐蚀、开运算、闭运算等操作，以便更好地分离出车牌区域。

se = strel('rectangle', [5, 5]);
dilated_img = imdilate(denoised_img, se);
eroded_img = imerode(dilated_img, se);
opened_img = imopen(eroded_img, se);
closed_img = imclose(opened_img, se);

3. 对图像进行连通区域分析，找到车牌区域。

cc = bwconncomp(closed_img);
stats = regionprops(cc, 'Area', 'BoundingBox');
areas = [stats.Area];
[max_area, max_idx] = max(areas);
bounding_box = stats(max_idx).BoundingBox;
plate_img = imcrop(img, bounding_box);

4. 对车牌图像进行字符分割，例如基于投影法、基于连通区域分析等方法。

gray_plate = rgb2gray(plate_img);
binary_plate = imbinarize(gray_plate);
vertical_projection = sum(binary_plate, 1);
threshold = max(vertical_projection) * 0.5;
separation_points = find(vertical_projection > threshold);
separation_points_diff = diff(separation_points);
char_width = median(separation_points_diff);
char_boxes = [];
for i = 1:length(separation_points)-1
    char_box = [separation_points(i), 0, separation_points_diff(i), size(binary_plate, 1)];
    char_boxes = [char_boxes; char_box];
end

5. 对每个字符图像进行特征提取和分类，例如基于模板匹配、基于神经网络、基于支持向量机等方法。

for i = 1:size(char_boxes, 1)
    char_box = char_boxes(i, :);
    char_img = imcrop(binary_plate, char_box);
    char_feature = extract_feature(char_img);
    char_label = classify(char_feature);
    fprintf('%s', char_label);
end