This example show new features of IPCV 1.1 for features detection and matching.
- First, we will use the image in the demo folder “balloons_gray.png” for this example. Read the image and rotate it by 45 degree, and resize it to 70% of the original size.
–> S = imread(fullpath(getIPCVpath() + “/images/balloons_gray.png”));
–> S2 = imrotate(S,45);
–> S2 = imresize(S2,0.7);
- Next, use the ORB-Oriented FAST and Rotated BRIEF Algorithm to detect features, and plot them on the image.
–> f1 = imdetect_ORB(S);
–> f2 = imdetect_ORB(S2);
- This is followed by extracting the descriptors for both images, and perform matching on them.
–> d1 = imextract_DescriptorORB(S,f1);
–> d2 = imextract_DescriptorORB(S2,f2);
–> m = immatch_BruteForce(d1,d2);
- Finally, select 10 best matched points and show the matching!
–> [fout1,fout2,mout] = imbestmatches(f1,f2,m,10);
–> SS = imdrawmatches(S,S2,fout1,fout2,mout);