feature-detection

I'm using OpenCV 2.3 for keypoints detection and matching. But I am a bit confused with the size and response parameters given by the detection algorithm. What do they exactly mean? Based on the OpenCV manual, I can't figure it out: float size : diameter of the meaningful keypoint neighborhood float response : the response by which the most strong keypoints have been selected. Can be used for further sorting or subsampling I thought the best point to track would be the one with the highest response but it seems that it is not the case. So how could I subsample the set of key points returned by

Does anyone know the difference between FeatureDetection and DescriptorExtraction in OpenCV 2.3? I understand that the latter is required for matching using DescriptorMatcher. If that's the case, what is FeatureDetection used for? Thank you. Feature detection In computer vision and image processing the concept of feature detection refers to methods that aim at computing abstractions of image information and making local decisions at every image point whether there is an image feature of a given type at that point or not. The resulting features will be subsets of the image domain, often in the

I am trying to extract features using OpenCV's HoG API, however I can't seem to find the API that allow me to do that. What I am trying to do is to extract features using HoG from all my dataset (a set number of positive and negative images), then train my own SVM. I peeked into HoG.cpp under OpenCV, and it didn't help. All the codes are buried within complexities and the need to cater for different hardwares (e.g. Intel's IPP) My question is: Is there any API from OpenCV that I can use to extract all those features / descriptors to be fed into a SVM ? If there's how can I use it to train my

I am using the ORB feature detector to find matches between two images using this code: FeatureDetector detector = FeatureDetector.create(FeatureDetector.ORB); DescriptorExtractor descriptor = DescriptorExtractor.create(DescriptorExtractor.ORB);; DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING); // First photo Imgproc.cvtColor(img1, img1, Imgproc.COLOR_RGB2GRAY); Mat descriptors1 = new Mat(); MatOfKeyPoint keypoints1 = new MatOfKeyPoint(); detector.detect(img1, keypoints1); descriptor.compute(img1, keypoints1, descriptors1); // Second photo Imgproc

What I have done : Taken input image and resized image to standard size as I have to compare it with template. converted in binary using threshold value. Detected connected component and displayed largest component. As it is whole hand.as shows here: Place image at same coordinates to check placement of finger for comparison with template image but their positioning is different purple one is template image I m doing comparison of image with Image subtraction method. These case will not able to predict if their is hairline fracture as their are many small lines detected in image. Is their any