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% Harris角点提取算法 %
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clear;
filename = 'ok.jpg';
X = imread(filename); % 读取图像
% imshow(X)
Info = imfinfo(filename); %获取图像相关信息
if (Info.BitDepth > 8)
f = rgb2gray(X);
end
%《基于特征点的图像配准与拼接技术研究》
%计算图像亮度f(x,y)在点(x,y)处的梯度-----------------------------------------------
ori_im = double(f) / 255; %unit8转化为64为双精度double64
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 = [-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
%构造自相关矩阵---------------------------------------------------------------
Ix2 = Ix .^ 2;
Iy2 = Iy .^ 2;
Ixy = Ix .* Iy;
clear Ix;
clear Iy;
h= fspecial('gaussian', [7 7], 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);
%提取特征点---------------------------------------------------------------
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
k = 0.06; %k为常系数，经验取值范围为0.04~0.06
for i = 1 : height
for j = 1 : width
M = [Ix2(i, j) Ixy(i, j); Ixy(i, j) Iy2(i, j)]; % auto correlation matrix
R(i,j) = det(M) - k * (trace(M)) ^ 2; % 计算R
if R(i,j) > Rmax
Rmax = R(i, j);
end;
end;
end;
T = 0.01 * Rmax;%固定阈值，当R(i, j) > T时，则被判定为候选角点
%在计算完各点的值后，进行局部非极大值抑制-------------------------------------
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) > T && 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;
i = 1;
for j = 1 : height
for k = 1 : width
if result(j, k) == 1;
corners1(i, 1) = j;
corners1(i, 2) = k;
i = i + 1;
end;
end;
end;
[posc, posr] = find(result == 1);
figure,imshow(ori_im);
hold on;
plot(posr, posc, 'g.');
