Harris Corrner Detector Matlab codeThe above image is the necessary condition for writing the MATLAB code.

The following is the code I wrote.

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function [corner, C] = detectHarrisCorner(im, fx_operator, fy_operator, ...

Gaussian_sigma, alpha, C_thres, NMS_ws)

im= rgb2gray(im);

[h,w]=size(im);

fx=conv2(im,fx_operator,'same'); fy=conv2(im,fy_operator,'same');

fxy=fx.*fy; fx2=fx.*fx; fy2=fy.*fy;

fxy2=conv2(fxy,GaussianFilter(Gaussian_sigma),'same'); fx2=conv2(fx2,GaussianFilter(Gaussian_sigma),'same'); fx2=conv2(fx2,GaussianFilter(Gaussian_sigma),'same');

detM=(fx2.*fy2)-(fxy2.^2); trM=(fx2+fy2).^2;

C=detM-(alpha*trM); threshold= C_thres*max(C); % C_thres=0.05

C=(C>threshold);

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What code should I add to satisfy the above condition number 6? Condition 6 relates to non maximum suppression.

I want the correct code.

Also, please capture the image of the code, plz

Harris Corner Detector 1. Convert RGB image to Gray image 2. Compute derivatives (fx,fy) 3. Compute fa?, fjs, fxfy 4. Apply Gaussian filtering to fa?, fy?, fxfy 5. Compute cornerness score map C = g(f?)g(f;?) g(fxf,)? a[g(f2) + g(4,2))? 6. Apply threshold & non-maximum suppression. To be interest point, cornerness score > threshold AND cornerness score should be maximum among 7 x 7 window, Harris Corner Detector 1. Convert RGB image to Gray image 2. Compute derivatives (fx,fy) 3. Compute fa?, fjs, fxfy 4. Apply Gaussian filtering to fa?, fy?, fxfy 5. Compute cornerness score map C = g(f?)g(f;?) g(fxf,)? a[g(f2) + g(4,2))? 6. Apply threshold & non-maximum suppression. To be interest point, cornerness score > threshold AND cornerness score should be maximum among 7 x 7 window