sift

问题 The SIFT descriptor is a local descriptor that introduced by David Lowe. This descriptor can be splitted up into multiple parts: 1- Constructing a scale space 2- LoG Approximation 3- Finding keypoints 4- Get rid of bad key points 5- Assigning an orientation to the keypoints 6- Generate SIFT features So, my question is: What is the computational complexity of SIFT descriptor? something like O(2n+logn) 回答1: Here's a paper that talks exactly about this. The actual time complexity for a n by n

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 am trying to dump keypoints of cars with SIFT or SURF and match these keypoints to a video in order to detect cars. Keypoints are more convenient to use instead of Haar Cascades because I would have to use a lot of images for example 5000 to train, which will take a lot of computation process. Keypoints from SURF or SIFT are scale invariant which will be almost the same in every car. The code for dumping keypoints into a txt file is: import cv2 import numpy as np import os import cPickle

问题 I have followed OpenCV Feature Detection and Description tutorial and used SIFT and other algorithms in OpenCV to find matching feature points between 2 images. From what i understood, these algorithms can find the similar regions between 2 images. But i am interested in identifying the different or dis-similar regions. How can i draw all the NON-MATCHING feature points on both the images? Further more, can i draw boundaries around these non-matching points to be able to show which regions in

问题 I would like to find a way which would allow to compare a SIFT descriptor of an image (query) with descriptors in a SQL database which contains lots of descriptors of differents pictures. In fact, my final purpose is to do an application which allow to compare one picture with lots of picture in a database (not in my device) but in a SQL database. The first thing i thought was to stock each descriptors on the SQL database and compare each descriptors to another one using the Brute Force

问题 I would like to find a way which would allow to compare a SIFT descriptor of an image (query) with descriptors in a SQL database which contains lots of descriptors of differents pictures. In fact, my final purpose is to do an application which allow to compare one picture with lots of picture in a database (not in my device) but in a SQL database. The first thing i thought was to stock each descriptors on the SQL database and compare each descriptors to another one using the Brute Force

问题 I am reading lot of post for object detection using feature extraction (sift ecc). After having calculate descriptors on both images, to get good matches they are using crossCheckMatching. (found on sample/cpp/descritpor_extractor_matcher.cpp) Coudl I understand why this choice? Why we need to evalute both descriptorMatcher->knnMatch( descriptors1, descriptors2, matches12, knn ); descriptorMatcher->knnMatch( descriptors2, descriptors1, matches21, knn ); I don't understand it. Computing the

问题 I'm trying to compare 2 images that are taken from a digital camera. Since there may be movement on the camera, I want to first make the pictures "match" and then compare (using some distant function). To match them, I'm thinking about cropping the second picture and using SIFT to find it inside the first picture... it will probably have a small difference on scale/translation/rotation so then I'd need to find the transformation matrix that converts image 1 to image 2 (based on points found

问题 i'm trying to evaluate SIFT and SURF Detectors by Repeatability criteria. i find out that below method can find Repeatability ,Correspondence of SIFT and SURF cv::evaluateFeatureDetector(img_1c, img_2c, h12, &key_points_1, &key_points_2, repeatability, corrCounter); some of the result are listed below: Number Repeatibility Correspond Keypoint 1st Keypoint 2th 1to2 0.7777778 140 224 180 1to3 0.7125 114 224 161 1to4 0.704918 86 224 123 1to5 0.6853933 61 224 89 1to6 0.6521739 45 224 69 for first

问题 Matlab implementation of SIFT features were found from http://www.cs.ubc.ca/~lowe/keypoints/. with the help of stackoverflow. I want to save features to a .mat file. Features are roundness, color, no of white pixel count in the binary image and sift features. For the sift features I took descriptors in above code { [siftImage, descriptors, locs] = sift(filteredImg) } So my feature vector now is FeaturesTest = [roundness, nWhite, color, descriptors, outputs]; When saving this to .mat file