feature-detection

问题 I am trying to extract different point descriptors (SIFT, SURF, ORB, BRIEF,...) to build Bag of Visual words. The problem seems to be that I am using very small images : 12x60px. Using a dense extractor I am able to get some keypoints, but then when extracting the descriptor no data is extracted. Here is the code : vector<KeyPoint> points; Mat descriptor; // descriptor of the current image Ptr<DescriptorExtractor> extractor = DescriptorExtractor::create("BRIEF"); Ptr<FeatureDetector> detector

问题 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

问题 What are the steps in feature-based registration? If I have a transformed image matching to a stack of images (of which only one is the best match), what are the steps in registration? Once I find matches and use them to estimate a transform, what's usually the next step? 回答1: Find points/areas which are invariant to different kind of transformations like: translation, rotation, scale.. because this points are invariant to those transformation we can find them in the different images that we

问题 Which method is the best to compare two images and discard outliers points? In find_obj.cpp opencv example, they use FLANN, but don't discard outliers. I have seen some methods like using Kmeans or graphs. 回答1: There is a fairly reliable and efficient way to both reject noisy points and determine the transformation between your points of interest. The algorithm that is usually used to reject outliers is known as RANSAC (http://en.wikipedia.org/wiki/RANSAC), and the algorithm used to determine

问题 I have a whole bunch of images of illustrations that I would like to crop to a smaller preview size. The problem is that I want to crop them to show an "interesting" part of the illustration (ie avoid areas of whitespace). The images typically have a flat color or a subtle gradient for the background. They are mostly vector style artwork with fairly distinct shapes. Here are some examples: link ;-) I've been thinking about using some sort of image feature detection algorithm with a sliding

问题 I'm working on a project in which we may be using an Oracle 11g or an Oracle 12c database depending on what's available in various environments -- not ideal I know :( My project is a Spring Boot application and I'm using Liquibase to manage the database objects. My problem is that if I'm running on an Oracle 12c database, I'd like to take advantage of auto incrementing IDs while on Oracle 11g, I'll have to rely on a trigger and a sequence. Is there a way to detect if the current version of

问题 How can I improve the performance of the following circle-detection code from matplotlib.pyplot import imshow, scatter, show import cv2 image = cv2.imread('points.png', 0) _, image = cv2.threshold(image, 254, 255, cv2.THRESH_BINARY) image = cv2.Canny(image, 1, 1) imshow(image, cmap='gray') circles = cv2.HoughCircles(image, cv2.HOUGH_GRADIENT, 2, 32) x = circles[0, :, 0] y = circles[0, :, 1] scatter(x, y) show() with the following source image: I have tried adjusting the parameters of the

问题 How can I improve the performance of the following circle-detection code from matplotlib.pyplot import imshow, scatter, show import cv2 image = cv2.imread('points.png', 0) _, image = cv2.threshold(image, 254, 255, cv2.THRESH_BINARY) image = cv2.Canny(image, 1, 1) imshow(image, cmap='gray') circles = cv2.HoughCircles(image, cv2.HOUGH_GRADIENT, 2, 32) x = circles[0, :, 0] y = circles[0, :, 1] scatter(x, y) show() with the following source image: I have tried adjusting the parameters of the

问题 According to my research: "WebView" can disable "opening links in new windows/tabs". WebView is used by native app developers to display webpages within their app(see Twitter's app). Detecting WebView via user agent doesn't work consistently and isn't a best practice anyway. Simply attempting to open a new window with JS triggers popup blockers; making it an unreliable way to test if a new window can be opened. I need to detect when this feature is not available. Impossible? Additional

问题 I'm spending considerable time in making my UI to work with keyboard input only. But in the end I'm not sure whether I can rely on the assumption that Android devices all have touch screens. Is there a way to determine if an Android device has a touch screen? 回答1: You should research the existing devices and read the Android Compatibility Definition Document (CDD) and decide for yourself. I have spent some considerable time trying to figure out this problem for myself. The posters above are