OpenCV - 使用 SURF 描述符和 BruteForceMatcher 进行对象匹配

OpenCV - Object matching using SURF descriptors and BruteForceMatcher(OpenCV - 使用 SURF 描述符和 BruteForceMatcher 进行对象匹配)

问题描述

我有一个关于与 OpenCV 匹配的对象的问题.我正在使用 opencv 2.3 中实现的 SURF 算法首先检测每个图像上的特征，然后提取这些特征的描述符.使用Brute Force Matcher匹配的问题，我不知道我如何判断两个图像是否匹配，就像我使用两个不同的图像时，两个图像中的描述符之间有线条！

I have a question about objects matching with OpenCV. I'm useing SURF algorithm implemented in opencv 2.3 to first detect features on each image, and then extracting the descriptors of these features. The problem in matching using Brute Force Matcher, I don't know how I judge that the two images are matched or not that's as when I'm using two different images there are lines between descriptors in the two images!

我的代码的这些输出，无论是两个图像 - 我与它们进行比较 - 相似或不同，结果图像表明这两个图像匹配.

These outputs of my code, either the two images -I compare with them - are similar or different, the result image indicate that the two images are matched.

问题是:如何区分两张图片?

真匹配:

错误匹配！！:

我的代码:

Mat image1, outImg1, image2, outImg2;

// vector of keypoints
vector<KeyPoint> keypoints1, keypoints2;

// Read input images
image1 = imread("C://Google-Logo.jpg",0);
image2 = imread("C://Alex_Eng.jpg",0);

SurfFeatureDetector surf(2500);
surf.detect(image1, keypoints1);
surf.detect(image2, keypoints2);
drawKeypoints(image1, keypoints1, outImg1, Scalar(255,255,255), DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
drawKeypoints(image2, keypoints2, outImg2, Scalar(255,255,255), DrawMatchesFlags::DRAW_RICH_KEYPOINTS);

namedWindow("SURF detector img1");
imshow("SURF detector img1", outImg1);

namedWindow("SURF detector img2");
imshow("SURF detector img2", outImg2);

SurfDescriptorExtractor surfDesc;
Mat descriptors1, descriptors2;
surfDesc.compute(image1, keypoints1, descriptors1);
surfDesc.compute(image2, keypoints2, descriptors2);

BruteForceMatcher<L2<float>> matcher;
vector<DMatch> matches;
matcher.match(descriptors1,descriptors2, matches);

nth_element(matches.begin(), matches.begin()+24, matches.end());
matches.erase(matches.begin()+25, matches.end());

Mat imageMatches;
drawMatches(image1, keypoints1, image2, keypoints2, matches, imageMatches, Scalar(255,255,255));

namedWindow("Matched");
imshow("Matched", imageMatches);

cv::waitKey();
return 0;

推荐答案

问题在于仅使用 Brute Force Matcher，我在 "OpenCV 2 计算机视觉应用程序中找到了在两个视图之间获得一组良好匹配的方法编程手册"

The problem was in using Brute Force Matcher only, I found methods to obtain a set of good matches between two views at "OpenCV 2 Computer Vision Application Programming Cookbook"

第 9 章:使用随机样本共识匹配图像

Ch9: Matching images using random sample consensus

他们正在使用 K-Nearest Neighbor 和 RANSAC

They are using K-Nearest Neighbor and RANSAC

谢谢

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