Harris角点提取算法和改进的Harris角点提取算法
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Harris角点提取算法 %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear;
ori_im = imread('arroyo-r.tiff'); % 读取图像
% fx = ; % 高斯函数一阶微分，x方向(用于改进的Harris角点提取算法)
fx = [-2 -1 0 1 2]; % x方向梯度算子(用于Harris角点提取算法)
Ix = filter2(fx,ori_im); % x方向滤波
% fy = ; % 高斯函数一阶微分，y方向(用于改进的Harris角点提取算法)
fy = [-2;-1;0;1;2]; % y方向梯度算子(用于Harris角点提取算法)
Iy = filter2(fy,ori_im); % y方向滤波
Ix2 = Ix.^2;
Iy2 = Iy.^2;
Ixy = Ix.*Iy;
clear Ix;
clear Iy;
h= fspecial('gaussian',,2); % 产生7*7的高斯窗函数，sigma=2
Ix2 = filter2(h,Ix2);
Iy2 = filter2(h,Iy2);
Ixy = filter2(h,Ixy);
height = size(ori_im,1);
width = size(ori_im,2);
result = zeros(height,width); % 纪录角点位置，角点处值为1
R = zeros(height,width);
Rmax = 0; % 图像中最大的R值
for i = 1:height
for j = 1:width
M = ; % auto correlation matrix
R(i,j) = det(M)-0.06*(trace(M))^2; % 计算R
if R(i,j) > Rmax
Rmax = R(i,j);
end;
end;
end;
cnt = 0;
for i = 2:height-1
for j = 2:width-1
% 进行非极大抑制，窗口大小3*3
if R(i,j) > 0.01*Rmax && R(i,j) > R(i-1,j-1) && R(i,j) > R(i-1,j) && R(i,j) > R(i-1,j+1) && R(i,j) > R(i,j-1) && R(i,j) > R(i,j+1) && R(i,j) > R(i+1,j-1) && R(i,j) > R(i+1,j) && R(i,j) > R(i+1,j+1)
result(i,j) = 1;
cnt = cnt+1;
end;
end;
end;
= find(result == 1);
cnt % 角点个数
imshow(ori_im);
hold on;
plot(posr,posc,'r+'); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 改进的Harris角点提取算法 %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear;
ori_im = imread('arroyo-r.tiff'); % 读取图像
% fx = ; % 高斯函数一阶微分，x方向(用于改进的Harris角点提取算法)
fx = [-2 -1 0 1 2]; % x方向梯度算子(用于Harris角点提取算法)
Ix = filter2(fx,ori_im); % x方向滤波
% fy = ; % 高斯函数一阶微分，y方向(用于改进的Harris角点提取算法)
fy = [-2;-1;0;1;2]; % y方向梯度算子(用于Harris角点提取算法)
Iy = filter2(fy,ori_im); % y方向滤波
Ix2 = Ix.^2;
Iy2 = Iy.^2;
Ixy = Ix.*Iy;
clear Ix;
clear Iy;
h= fspecial('gaussian',,2); % 产生7*7的高斯窗函数，sigma=2
Ix2 = filter2(h,Ix2);
Iy2 = filter2(h,Iy2);
Ixy = filter2(h,Ixy);
height = size(ori_im,1);
width = size(ori_im,2);
result = zeros(height,width); % 纪录角点位置，角点处值为1
R = zeros(height,width);
for i = 1:height
for j = 1:width
M = ; % auto correlation matrix
R(i,j) = det(M)-0.06*(trace(M))^2;
end;
end;
cnt = 0;
for i = 2:height-1
for j = 2:width-1
% 进行非极大抑制，窗口大小3*3
ifR(i,j) > R(i-1,j-1) && R(i,j) > R(i-1,j) && R(i,j) > R(i-1,j+1) && R(i,j) > R(i,j-1) && R(i,j) > R(i,j+1) && R(i,j) > R(i+1,j-1) && R(i,j) > R(i+1,j) && R(i,j) > R(i+1,j+1)
result(i,j) = 1;
cnt = cnt+1;
end;
end;
end;
Rsort=zeros(cnt,1);
= find(result == 1);
for i=1:cnt
Rsort(i)=R(posr(i),posc(i));
end;
=sort(Rsort,1);
Rsort=flipud(Rsort);
ix=flipud(ix);
ps=100;
posr2=zeros(ps,1);
posc2=zeros(ps,1);
for i=1:ps
posr2(i)=posr(ix(i));
posc2(i)=posc(ix(i));
end;
imshow(ori_im);
hold on;
plot(posc2,posr2,'r+'); 谢谢，大有收获 {:{39}:}{:{39}:} 楼主太厉害了
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