c語言數字圖像處理（九）：邊緣檢測

時間 2019-12-14

標籤 c語言數字圖像處理邊緣檢測简体版

原文原文鏈接

背景知識

邊緣像素是圖像中灰度突變的像素，而邊緣是鏈接邊緣像素的集合。邊緣檢測是設計用來檢測邊緣像素的局部圖像處理方法。html

孤立點檢測

使用<https://www.cnblogs.com/GoldBeetle/p/9744625.html>中介紹的拉普拉斯算子算法

輸出圖像爲函數

卷積模板測試

以前有過代碼實現，這篇文章中再也不進行測試ui

基本邊緣檢測

圖像梯度spa

梯度向量大小設計

在圖像處理過程當中，因平方和和開方運算速度較慢，所以簡化爲以下計算方法3d

梯度向量方向與x軸夾角code

對應與不一樣的偏導數計算方法，得出邊緣檢測的不一樣模板htm

檢測垂直或水平邊緣

原圖

使用Sobel模板檢測水平邊緣

使用Sobel模板檢測垂直邊緣

二者相加

代碼實現

 1 void edge_detection(short** in_array, short** out_array, long height, long width)
 2 {
 3     short gx = 0, gy = 0;
 4     short** a_soble1;
 5     short** a_soble2;
 6 
 7     a_soble1 = allocate_image_array(3, 3);
 8     a_soble2 = allocate_image_array(3, 3);
 9     for (int i = 0; i < 3; i++){
10         for (int j = 0; j < 3; j++){
11             a_soble1[i][j] = soble1[i][j];
12             a_soble2[i][j] = soble2[i][j];
13         }
14     }
15     for (int i = 0; i < height; i++){
16         for (int j = 0; j < width; j++){
17             gx = convolution(in_array, i, j, height, width, a_soble1, 3);
18             gy = convolution(in_array, i, j, height, width, a_soble2, 3);
19             // out_array[i][j] = gx;
20             // out_array[i][j] = gy;
21             out_array[i][j] = gx + gy;
22             if (out_array[i][j] < 0)
23                 out_array[i][j] = 0;
24             else if (out_array[i][j] > 0xff)
25                 out_array[i][j] = 0xff;
26         }
27     }
28     free_image_array(a_soble1, 3);
29     free_image_array(a_soble2, 3);
30 }

檢測對角邊緣

Sobel 45°檢測模板

Sobel -45°檢測模板

二者相加

代碼實現通上，只需替換模板值便可

Marr-Hildreth邊緣檢測算法

1. 對二維高斯函數進行取樣，得高斯低通濾波器，對輸入圖像濾波，濾波器模板大小爲大於等於6*σ的最小奇整數

算法實現

 1 void generate_gaussian_filter(double** gaussian_filter, long sigma)
 2 {
 3     double x, y;
 4     long filter_size = 6 * sigma + 1;
 5 
 6     for (int i = 0; i < filter_size; i++){
 7         for (int j = 0; j < filter_size; j++){
 8             x = i - filter_size / 2;
 9             y = j - filter_size / 2;
10             gaussian_filter[i][j] = exp(-1.0 * ((pow(x, 2) + pow(y, 2)) / 2 * sigma * sigma));
11         }
12     }
13 }

2. 計算第一步獲得圖像的拉普拉斯，利用以下模板

算法實現

 1 void laplace(short** in_array, short** out_array, long height, long width)
 2 {
 3     short** a_sharpen;
 4 
 5     a_sharpen = allocate_image_array(3, 3);
 6     for (int i = 0; i < 3; i++){
 7         for (int j = 0; j < 3; j++){
 8             a_sharpen[i][j] = sharpen[i][j];
 9         }
10     }
11     for (int i = 0; i < height; i++){
12         for (int j = 0; j < width; j++){
13             out_array[i][j] = convolution(in_array, i, j, height, width, a_sharpen, 3);
14         }
15     }
16     free_image_array(a_sharpen, 3);
17 }

運行結果

3. 尋找零交叉，對任意像素p，測試上/下，左/右，兩個對角線四個位置，當有兩對符號不一樣而且絕對值差大於某一閾值時爲零交叉點

算法實現

 1 int is_cross(short** in_array, long row, long column)
 2 {
 3     int cross_num = 0;
 4 
 5     if (in_array[row-1][column-1] * in_array[row+1][column+1] < 0 && 
 6         abs(abs(in_array[row-1][column-1]) - abs(in_array[row+1][column+1])) > 0x66)
 7         cross_num++;
 8     if (in_array[row-1][column] * in_array[row+1][column] < 0&& 
 9         abs(abs(in_array[row-1][column]) - abs(in_array[row+1][column])) > 0x66)
10         cross_num++;
11     if (in_array[row-1][column+1] * in_array[row+1][column-1] < 0&& 
12         abs(abs(in_array[row-1][column+1]) - abs(in_array[row+1][column-1])) > 0x66)
13         cross_num++;
14     if (in_array[row][column-1] * in_array[row][column+1] < 0&& 
15         abs(abs(in_array[row][column-1]) - abs(in_array[row][column+1])) > 0x66)
16         cross_num++;
17 
18     if (cross_num >= 2)
19         return 1;
20     else
21         return 0;
22 }

 1 void marr(short** in_array, short** out_array, long height, long width)
 2 {
 3     long sigma = 2;
 4     long filter_size = 6 * sigma + 1;
 5     double** gaussian_filter;
 6     short **gauss_array, **laplace_array;
 7 
 8     gaussian_filter = allocate_double_array(filter_size, filter_size);
 9     gauss_array = allocate_image_array(height, width);
10     laplace_array = allocate_image_array(height, width);
11     generate_gaussian_filter(gaussian_filter, sigma);
12 
13     for (int i = 0; i < height; i++){
14         for (int j = 0; j < width; j++){
15             gauss_array[i][j] = convolutiond(in_array, i, j, height, width, gaussian_filter, filter_size);
16         }
17     }
18     printf("Gasuuian filter done\n");
19     laplace(gauss_array, laplace_array, height, width);
20     printf("Laplace done\n");
21     zero_cross(laplace_array, out_array, height, width);
22     printf("Zero cross done\n");
23     
24     free_double_array(gaussian_filter, filter_size);
25     free_image_array(gauss_array, height);
26     free_image_array(laplace_array, height);
27 }