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我想使用OpenCV 2.4.0来跟踪一系列灰度图像中的特征点集。特征点跟踪
我已经知道如何实现SIFT或SURF来检测特征点并最初计算描述符。但是，我需要帮助计算一个特征点的SIFT描述符，其位置（u，v）仅为我所知。下面显示了SIFT的工作示例代码。
例如，如果我使用哈里斯角检测器在dv_scenePoints_t像检测功能：
cvGoodFeaturesToTrack (source2, eig_img, temp_img, dv_scenePoints_t, &corner_count, 0.3, 3.0, mask, 7, 1);
在这样的情况下。然后，我将如何计算点的SIFT描述在dv_scenePoints_t。
此外，如果我必须通过粒子滤波器跟踪特征点。那么，我将如何使用SIFT描述符来计算每个粒子的权重（特征点假设）。 谢谢。
#include "stdafx.h"
#include <stdio.h>
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/nonfree/nonfree.hpp"
#include <opencv2/nonfree/features2d.hpp>
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/legacy/legacy.hpp"
#include "opencv2/legacy/compat.hpp"
#include <opencv/cv.h>
#include <opencv/highgui.h>
#include <string.h>
#include <iostream>
using namespace cv;
using namespace std;
int main(int argc, char *argv[])
{
Mat source1 = imread("KITTI_train.png",CV_LOAD_IMAGE_GRAYSCALE);
Mat source2 = imread("KITTI_trainRotate90.png",CV_LOAD_IMAGE_GRAYSCALE);
vector<KeyPoint> dv_sceneKeypoints_t, dv_objectKeypoints_t;
vector<DMatch> matches;
SiftFeatureDetector detector(400,5,0.03);
detector.detect(source1, dv_objectKeypoints_t);
detector.detect(source2, dv_sceneKeypoints_t);
SiftDescriptorExtractor extractor;
Mat descriptors1,descriptors2;
extractor.compute(source1,dv_objectKeypoints_t,descriptors1);
extractor.compute(source2,dv_sceneKeypoints_t,descriptors2);
FlannBasedMatcher matcher;
matcher.match(descriptors1,descriptors2, matches);
Mat target;
drawMatches(source1,dv_objectKeypoints_t,source2,dv_sceneKeypoints_t,matches,target);
imshow("Matches", target);
waitKey(0);
return 0;
}