Extraction method of Harris Corner point for feature point extraction

1. Significance of Feature point extraction
2. Corner Point


3. The fundamentals of Harris Corner Point detection


steps for 4.Harris corner detection algorithm








design of 5.Harris corner point extraction algorithm

<span style= "FONT-SIZE:18PX;" >function [Ptx,pty] = harrispoints (imgin,threshold)% Harris Corner point extraction algorithm% computed image brightness f (x, Y) at point (x, y)  The gradient-----------------------------------------FX = [5 0-5;8 0-8;5 0-5];            % Gauss function First order differential, x direction (for improved Harris)%FX = [-2-1 0 1 2];      % x Direction gradient operator (for Harris Corner Point extraction algorithm) Ix = Filter2 (FX, Imgin); % x Direction filter FY = [5 8 5;0  0 0;-5-8-5];        % Gauss function First order differential, Y direction (for improved Harris)%fy = [-2;-1; 0; 1; 2];      % y-direction gradient operator (for Harris Corner Point extraction algorithm) Iy = Filter2 (FY, imgin);  
% y-direction filter% constructed autocorrelation matrix-------------------------------------------------------------Ix2 = Ix ^ 2;  
Iy2 = Iy. ^ 2;  
Ixy = Ix. * IY;  
Clear Ix;  
Clear Iy;  
H= fspecial (' Gaussian ', [7 7], 2);% produces 7*7 Gaussian window function, sigma=2 Ix2 = Filter2 (h,ix2);  
Iy2 = Filter2 (h,iy2);  
Ixy = Filter2 (H,IXY);  
% extract feature point-----------------------------------------------------------------height = size (imgin, 1);  
width = Size (imgin, 2);  
result = Zeros (height, width),% record corner position, point value at 1 R = zeros (height, width);  Rmax = 0; %The largest R value in the image is k = 0.05; %k is the constant coefficient, the empirical value range is 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;  
        % Calculation R if R (i,j) > Rmax Rmax = R (i, j);  
    End  
End  
End   
T = threshold* rmax;% fixed threshold, when R (I, J) >t, is determined to be the candidate corner% after calculating the values of each point, the local non-maximal value inhibition-------------------------------------cnt = 0; For i = 2:height-1 to J = 2:width-1% for non-maximum suppression, window size 3*3 if (R (i,j) >t && R (i,j) & Gt
            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 rows; column columns; end</span>

6. Experimental results show


7. Supplementary reading materials