feature-detection

SURF is patented, as is SIFT. ORB and BRIEF are not patented, but their features are not scale-invariant, seriously limiting their usefulness in complex scenarios. Are there any feature extractors that can extract scale-invariant features as fast as SURF and are not so strictly patented as SURF and SIFT? Rui Marques Although you already choose BRISK, you might find FREAK interesting. Author claims to have better results than BRISK and ORB. I should also add that ORB is scale-invariant but has some problems in that area. So I would still recommend it for someone to try it. The FREAK source code

I am new to opencv and trying to implement image matching between two images. For this purpose, I'm trying to understand the difference between feature descriptors, descriptor extractors and descriptor matchers. I came across a lot of terms and tried to read about them on the opencv documentation website but I just can't seem to wrap my head around the concepts. I understood the basic difference here. Difference between Feature Detection and Descriptor Extraction But I came across the following terms while studying on the topic : FAST, GFTT, SIFT, SURF, MSER, STAR, ORB, BRISK, FREAK, BRIEF I

I have a texture-heavy OpenGL game that I'd like to tune based on how much RAM the device has. The highest resolution textures I have work fine on an iPhone 4 or iPad2, but earlier devices crash in the middle of loading the textures. I have low-res versions of these textures, but I need to know when to use them. My current tactic is to detect specific older devices (3GS has a low-res screen; iPad has no camera), then only load the hi-res textures for IPad2 and above and iPhone 4 and above — and I suppose I'll need to do something for the iPod touch. But I'd much rather use feature detection

I want to match feature points in stereo images. I've already found and extracted the feature points with different algorithms and now I need a good matching. In this case I'm using the FAST algorithms for detection and extraction and the BruteForceMatcher for matching the feature points. The matching code: vector< vector<DMatch> > matches; //using either FLANN or BruteForce Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create(algorithmName); matcher->knnMatch( descriptors_1, descriptors_2, matches, 1 ); //just some temporarily code to have the right data structure vector< DMatch > good

[EDIT] I have devised some code for image comparison. The matching part is still a bit flawed and I would love some assitance. The project can be found at - GitHub . I have these two images Img1 and Img2 : When I use the following command in openCV Mat img1 = Highgui.imread("mnt/sdcard/IMG-20121228.jpg"); Mat img2 = Highgui.imread("mnt/sdcard/IMG-20121228-1.jpg"); try{ double l2_norm = Core.norm( img1, img2 ); tv.setText(l2_norm+""); } catch(Exception e) { //image is not a duplicate } I get a double value for l2_norm. This double value varies for duplicate image pairs. But if the images are