feature-extraction

问题 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? 回答1: 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

问题 My project is recognition the leaf on Android using OpenCV library. I am using ORB detection to get the keypoint of image and use ORB descriptor to get the feature of the keypoint. This is the code that i use: bmp=BitmapFactory.decodeResource(getResources(),R.drawable.t1); Utils.bitmapToMat(bmp, mat); FeatureDetector detector = FeatureDetector.create(FeatureDetector.ORB); detector.detect(mat, keypoints); DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.ORB);

问题 I am in the process of creating a small program which detects objects(small image) in the large image and I am using OpenCV java. As I have to consider rotation and scaling I have used FeatureDetector.BRISK and DescriptorExtractor.BRISK. Following approach is used to filter the match results to get the best matches only. I have two questions Is there a way to find the below min_dist and max_dist with the loop I have used? Most important question - Now the problem is I need to use these