feature-detection

I am trying to use OpenCV's feature detection tools in order to decide whether a small sample image exists in a larger scene image or not. I used the code from here as a reference (without the homography part). UIImage *sceneImage, *objectImage1; cv::Mat sceneImageMat, objectImageMat1; cv::vector<cv::KeyPoint> sceneKeypoints, objectKeypoints1; cv::Mat sceneDescriptors, objectDescriptors1; cv::SurfFeatureDetector *surfDetector; cv::FlannBasedMatcher flannMatcher; cv::vector<cv::DMatch> matches; int minHessian; double minDistMultiplier; minHessian = 400; minDistMultiplier= 3; surfDetector = new

I am building a spam filter using the NLTK in Python. I now check for the occurances of words and use the NaiveBayesClassifier resulting in an accuracy of .98 and F measure for spam of .92 and for non-spam: 0.98. However when checking the documents in which my program errors I notice that a lot of spam that is classified as non-spam are very short messages. So I want to put the length of a document as a feature for the NaiveBayesClassifier. The problem is it now only handles binary values. Is there any other way to do this than for example say: length<100 =true/false? (p.s. I have build the

I am trying to make a program that will find similar images from a dataset of images. The steps are extract SURF descriptors for all images store the descriptors Apply knn on the stored descriptors Match the stored descriptors to the query image descriptor using kNN Now each images SURF descriptor will be stored as Hierarchical k-means tree, now do I store each tree as a separate file or is it possible to build some sort of single tree with all the images descriptors and updated as images are added to dataset. This is the paper I am basing the program on. Use a KD-Tree instead. You will be

I'm trying the assess the correctness of my SURF descriptor implementation with the de facto standard framework by Mikolajczyk et. al . I'm using OpenCV to detect and describe SURF features, and use the same feature positions as input to my descriptor implementation. To evaluate descriptor performance, the framework requires to evaluate detector repeatability first. Unfortunately, the repeatability test expects a list of feature positions along with ellipse parameters defining the size and orientation of an image region around each feature. However, OpenCV's SURF detector only provides feature

I'm trying to use the ORB keypoint detector and it seems to be returning much fewer points than the SIFT detector and the FAST detector. This image shows the keypoints found by the ORB detector: and this image shows the keypoints found by the SIFT detection stage (FAST returns a similar number of points). Having such few points is resulting in very poor feature matching results across images. I'm just curious about the detection stage of ORB right now though because this seems like I'm getting incorrect results. I've tried using the ORB detector with default parameters and also custom

I'm working on a face feature detection project and I do detect the eyes, nose and mouth using OpenCv withHaarcascade xml files. But, I want to have the eyes and mouth corners points and the nose center. The goal is using it to predict emotions. I found this link that shows how it works, and I need to get to this result using JAVA. Could any one help me? Thanks in advance. http://cmp.felk.cvut.cz/~uricamic/flandmark/ in this part we receve the face image and we drawRect on the face: public void drawFaces(BufferedImage image) { final List<PotentialFace> faces = FacialRecognition.run(image, db);

I am currently trying to black out everything outside a circle. I am drawing the circle using the following lines of code: cv::Point center(cvRound(circles[i][0]), cvRound(circles[i][1])); // CVRound converts floating numbers to integer int radius = cvRound(circles[i][2]); // Radius is the third parameter [i][0] = x [i][1]= y [i][2] = radius circle( image, center, 3, cv::Scalar(0,255,0), -1, 8, 0 ); // Drawing little circle to Image Center , next Line of Code draws the real circle circle( image, center, radius, cv::Scalar(0,0,255), 3, 8, 0 ); // Circle(img, center, radius, color, thickness=1,