视觉算法笔记 - 边缘检测算法

忘是亡心i 2022-08-25 15:47 303阅读 0赞

方法

1 一阶梯度算子

正交梯度 ： \[1,-1\], \[1,-1\]';

robet: 斜线差分（四点差分）

prewitt: 平均差分

sobel: 加权平均差分

检测赋值阈值，可检测边缘的方向

2 二阶导数算子 laplacian算子

模板 \[0 -1 0; -1 4 -1;0 -1 0\]/4; 方向无关，检测零交叉，可要求过零点的两边的差值大于一定的阈值

3 LoG

高斯滤波后再进行二阶导数，可先对高斯函数求二阶导数然后得到离散化模板，之后用模板和图像卷积

4 canny

以上在matlab中都采用edge实现。

关于零交叉的检测，参考代码，应该是对\[- +\]和\[- 0 +\]都进行检测：

% Look for the zero crossings: +-, -+ and their transposes  
% We arbitrarily choose the edge to be the negative point  
\[rx,cx\] = find( b(rr,cc) < 0 & b(rr,cc+1) > 0 ...  
& abs( b(rr,cc)-b(rr,cc+1) ) > thresh ); % \[- +\]  
e((rx+1) + cx\*m) = 1;  
\[rx,cx\] = find( b(rr,cc-1) > 0 & b(rr,cc) < 0 ...  
& abs( b(rr,cc-1)-b(rr,cc) ) > thresh ); % \[+ -\]  
e((rx+1) + cx\*m) = 1;  
\[rx,cx\] = find( b(rr,cc) < 0 & b(rr+1,cc) > 0 ...  
& abs( b(rr,cc)-b(rr+1,cc) ) > thresh); % \[- +\]'  
e((rx+1) + cx\*m) = 1;  
\[rx,cx\] = find( b(rr-1,cc) > 0 & b(rr,cc) < 0 ...  
& abs( b(rr-1,cc)-b(rr,cc) ) > thresh); % \[+ -\]'  
e((rx+1) + cx\*m) = 1;

% Most likely this covers all of the cases. Just check to see if there  
% are any points where the LoG was precisely zero:  
\[rz,cz\] = find( b(rr,cc)==0 );  
if ~isempty(rz)  
% Look for the zero crossings: +0-, -0+ and their transposes  
% The edge lies on the Zero point  
zero = (rz+1) + cz\*m; % Linear index for zero points  
zz = find(b(zero-1) < 0 & b(zero+1) > 0 ...  
& abs( b(zero-1)-b(zero+1) ) > 2\*thresh); % \[- 0 +\]'  
e(zero(zz)) = 1;  
zz = find(b(zero-1) > 0 & b(zero+1) < 0 ...  
& abs( b(zero-1)-b(zero+1) ) > 2\*thresh); % \[+ 0 -\]'  
e(zero(zz)) = 1;  
zz = find(b(zero-m) < 0 & b(zero+m) > 0 ...  
& abs( b(zero-m)-b(zero+m) ) > 2\*thresh); % \[- 0 +\]  
e(zero(zz)) = 1;  
zz = find(b(zero-m) > 0 & b(zero+m) < 0 ...  
& abs( b(zero-m)-b(zero+m) ) > 2\*thresh); % \[+ 0 -\]  
e(zero(zz)) = 1;

5 方向梯度算子

可求得某个方向的最大值