标签:产生 ack cti iss data enter 梯度 term corners
<span style="font-size:18px;">function [ptx,pty] = HarrisPoints(ImgIn,threshold) % Harris角点提取算法 %计算图像亮度f(x,y)在点(x,y)处的梯度----------------------------------------- fx = [5 0 -5;8 0 -8;5 0 -5]; % 高斯函数一阶微分,x方向(用于改进的Harris) %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, ImgIn); % x方向滤波 fy = [5 8 5;0 0 0;-5 -8 -5]; % 高斯函数一阶微分,y方向(用于改进的Harris) %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, ImgIn); % 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(ImgIn, 1); width = size(ImgIn, 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 = 0.05; %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)]; 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 = threshold* 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。窗体大小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; [pty, ptx] = find(result == 1); %row 行;column 列; end</span>
标签:产生 ack cti iss data enter 梯度 term corners
原文地址:http://www.cnblogs.com/zhchoutai/p/7182438.html