sift

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 surf = cv2.xfeatures2d.SURF_create() descriptors = [] image = cv2.imread('1.jpg') kp, dsc = surf

I'm using the SIFT algorithm to determine the level of similarity between two images. I managed to get the list of descriptors for image but I cant seem to figure what to do next. My problem is that I have a different number of descriptors and their order is maybe not the same. What is the best way to compare these descriptors? Thanks. Basic euclidean distance together with a heuristic that stipulated that a match is only accepted if it is significantly better than the second best match. As advocated by the (co) inventer, Lowe For each descriptor in image A, find its nearest neighbor in image

i am using SIFT algorithm in matlab to determine the similarity between template image and a set of images, where in the end i have to determine the best match between set of images on the basis of SCORES,is it true to say that higher the score of an image the better the match? i am aware that when there is an exact match, the score is zero, but what if the image is similar? I assume by SCORES you mean a similarity measurement between a pair of matched descriptors (e.g. euclidean distance). If my assumption is correct, statistically averaging the scores of all matches (e.g. SSD) should give

I am trying to implement a BOW object recognition code in matlab. The process is slightly complicated and I've had a lot of trouble finding proper documentation on the procedure. So could someone double check if my plan below makes sense? I'm using the VLSIFT library extensively here Training: 1. Extract SIFT image descriptor with VLSIFT 2. Quantize the descriptors with k-means(vl_hikmeans) 3. Take quantized descriptors and create histogram(VL_HIKMEANSHIST) 4. Create SVM from histograms(VL_PEGASOS?) I understand step 1-3, but I'm not quite sure if the function for SVM is correct. VL_PEGASOS

I am doing an ancient coins recognition system using matlab. What I have done so far is: convert to grayscale remove noise using Gaussian filter contrast enhancement edge detection using canny edge detector. Now I want to extract feature for classification. Features I thought to select are roundness, area, colour, SIFT and SURF. My problem is how I can apply SIFT and SURF algorithms to my project. I couldn't find built-in functions for both. you can find a matlab implementation of SIFT features here: http://www.cs.ubc.ca/~lowe/keypoints/ You can find SIFT as a C implementation with MATLAB

I have read a lot of articles about implementing bag of words after taking sift features of an image, but I'm still confused what to do next. What do i specifically do? Thank you so much in advance for the guidance. This is the code that i have so far. cv::Mat mat_img = cropped.clone(); Mat grayForML; cvtColor(mat_img, grayForML, CV_BGR2GRAY); IplImage grayImageForML = grayForML.operator IplImage(); //create another copy of iplGray IplImage *input = cvCloneImage(&grayImageForML); Mat matInput = cvarrToMat(input); // Mat matInput = copy_gray.clone(); cv::SiftFeatureDetector detector; std:

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 method or the FlanneBased method. The problem is that,in a SQL database, it would take a long time to