解释一下下列代码result=zeros(x,y);%特征提取结果 for i=2:x-1 for j=2:y-1 dg1=abs(pic_bw(i,j)-pic_bw(i+1,j)); dg2=abs(pic_bw(i,j)-pic_bw(i,j+1)); dg3=abs(pic_bw(i,j)-pic_bw(i-1,j)); dg4=abs(pic_bw(i,j)-pic_bw(i,j-1)); dg=[dg1,dg2,dg3,dg4]; temp=sort(dg);%对四个差分算子进行升序排列 if temp(3)==1%有任意两个大于阈值,则该像素有可能是一特征点 result(i,j)=255;%是初选点 else result(i,j)=0;%不是初选点 end end end
时间: 2024-03-29 20:38:01 浏览: 11
这段代码是一个特征点提取的算法。首先,它创建了一个大小为x*y的矩阵result,用于记录特征点的位置。然后,对原始图像pic_bw的每个像素进行处理,计算该像素与其周围像素的差分值(即灰度值的差异),并将这些差分值进行排序。如果其中有两个差分值大于设定的阈值,那么该像素就被认为是一个特征点,将对应的矩阵result中的值设置为255(白色)。如果没有两个差分值同时大于阈值,那么该像素不是特征点,对应矩阵result中的值设置为0(黑色)。最终,result中白色像素的位置就是特征点的位置。
相关问题
注释每行代码 close all; warning off; % x=zeros(1000); y=zeros(1000); % flag=zeros(1000,1); % dist=zeros(1000,1000); % ch=zeros(1000,1); % ch1=zeros(32,1); % dist1=zeros(32,1); % match=zeros(1000,32); % neig=zeros(1000,1); % residual=zeros(1000); % energy=zeros(32,1); % join=zeros(32,32); % join1=zeros(32,1); distance=zeros(32,32); for i=1:1000 % x(i)=rand()*1000; y(i)=rand()*1000; residual(i)=2; end for i=1:1000 % plot(x(i),y(i),'o'); hold on; end for i=1:1000 % for j=1:1000 dist(i,j)=sqrt(power(((x(i)-x(j))),2)+power(((y(i)-y(j))),2)); end end for i=1:1000 %
Sure, here's the commented code:
```
% This line closes all open figures and turns off warnings
close all; warning off;
% These lines initialize variables with zeros
x = zeros(1000);
y = zeros(1000);
flag = zeros(1000,1);
dist = zeros(1000,1000);
ch = zeros(1000,1);
ch1 = zeros(32,1);
dist1 = zeros(32,1);
match = zeros(1000,32);
neig = zeros(1000,1);
residual = zeros(1000);
energy = zeros(32,1);
join = zeros(32,32);
join1 = zeros(32,1);
distance = zeros(32,32);
% This loop assigns random values between 0 and 1000 to each element of x and y, and sets residual to 2 for each element
for i = 1:1000
x(i) = rand()*1000;
y(i) = rand()*1000;
residual(i) = 2;
end
% This loop plots 1000 points on a 2D graph using the values of x and y
for i = 1:1000
plot(x(i),y(i),'o');
hold on;
end
% This loop calculates the distance between each pair of points using the Pythagorean theorem and stores the result in the dist array
for i = 1:1000
for j = 1:1000
dist(i,j) = sqrt(power(((x(i)-x(j))),2) + power(((y(i)-y(j))),2));
end
end
% This loop performs some operations on each element of the arrays
for i = 1:1000
% code for this loop is not provided
end
```
residual = zeros(1, n); for i = 1:n residual(i) = y(i) - C * x(:, i); end
这是一个计算向量残差的代码,其中:
- `n` 表示向量 `y` 和矩阵 `x` 的列数;
- `C` 是一个行数等于 `length(y)` 的向量,用于将 `x` 的列与 `y` 进行加权求和;
- `residual` 是一个长度为 `n` 的向量,用于存储计算结果。
具体来说,这段代码对于每一列 `i`,计算了 `y(i)` 减去 `C` 与 `x` 的第 `i` 列的内积,即残差,然后将结果存储在 `residual(i)` 中。最终得到的 `residual` 向量就是 `y` 与 `x` 通过 `C` 加权求和后的残差向量。