Feature point extraction algorithm is a basic technology in computer vision, which is used to extract unique and stable feature points from images. Common feature point extraction algorithms are as follows:
1. SIFT (Scale-Invariant Feature Transform) algorithm: SIFT algorithm is a feature point extraction algorithm based on scale space, which can extract feature points with stability under different scales, rotations and illumination changes. The SIFT algorithm includes steps such as scale space extremum detection, key point location, direction assignment, key point description, and feature point matching.
2. SURF (Speeded-Up Robust Feature) algorithm: The SURF algorithm is an accelerated version of the SIFT algorithm, which can increase the calculation speed while maintaining a high accuracy rate. In the SURF algorithm, the Hessian matrix is used to detect the local features of the image, and the feature description is realized by calculating the Haar wavelet response.
3. ORB (Oriented FAST and Rotated BRIEF) algorithm: ORB algorithm is a feature point extraction algorithm based on FAST corner detection and BRIEF descriptor, which has high speed and good performance. In the ORB algorithm, the FAST corner detection algorithm is used to detect the corners of the image, and the BRIEF algorithm is used to describe the feature points.
4. Harris corner detection algorithm: Harris corner detection algorithm is a feature point extraction algorithm based on image grayscale changes, which extracts corner feature points by calculating the corner response function of each point in the image.
5. Hessian-Laplace algorithm: The Hessian-Laplace algorithm is a feature point extraction algorithm based on the Hessian matrix. It calculates the Hessian matrix of the image to detect the local extreme points of the image, and then uses the Laplace operator to extract the feature points.
The above are common feature point extraction algorithms, and different algorithms are suitable for different scenarios and tasks.
The following is the implementation of SIFT algorithm in C++ language:
#include <opencv2/opencv.hpp>
using namespace cv;
int main()
{ // read image Mat img = imread("lena.png");
// Convert to grayscale image
Mat grayImg;
cvtColor(img, grayImg, COLOR_BGR2GRAY);
// Extract SIFT feature points
Ptr<Feature2D> sift = xfeatures2d::SIFT::create();
std::vector<KeyPoint> keypoints;
sift->detect(grayImg, keypoints);
// Display feature points
Mat imgWithKeypoints;
drawKeypoints(grayImg, keypoints, imgWithKeypoints);
imshow("SIFT keypoints", imgWithKeypoints);
waitKey(0);
return 0;
}
```
In the above code, the `imread` function is used to read the image, the `cvtColor` function converts the image into a grayscale image, the `xfeatures2d::SIFT::create()` function is used to create the SIFT algorithm object, and the `detect` function uses For extracting SIFT feature points, the `drawKeypoints` function is used to draw the feature points on the image, the `imshow` function is used to display the image, and the `waitKey` function is used to wait for the user to press a key.
The general process of image matching using SIFT algorithm is as follows:
1. Extract SIFT feature points from the image to be matched and the reference image respectively, which can be realized by using the SIFT algorithm mentioned above.
2. The feature description of the feature points in the two images can be realized by using the feature descriptor in the SIFT algorithm. The pixel values around each feature point are used as input, and the scale and direction of each pixel are calculated using the Gaussian difference pyramid to generate a 128-dimensional local feature vector.
3. Match the feature points in the two images. Generally, the nearest neighbor matching method is used, that is, for each feature point in the image to be matched, find the feature point closest to it in the reference image, and use it as the matching result.
4. Filter and optimize the matching results. Since the SIFT algorithm has high stability and good discrimination, the RANSAC (Random Sample Consensus) algorithm can be used to screen and optimize the matching results, remove mismatched points, and improve matching accuracy and robustness.
The following is a code that uses OpenCV to implement SIFT algorithm for image matching:
#include <opencv2/opencv.hpp>
using namespace cv;
int main()
{ // Read in the image to be matched and the reference image Mat img1 = imread("img1.jpg"); Mat img2 = imread("img2.jpg");
// Convert to grayscale image
Mat grayImg1, grayImg2;
cvtColor(img1, grayImg1, COLOR_BGR2GRAY);
cvtColor(img2, grayImg2, COLOR_BGR2GRAY);
// Extract SIFT feature points and feature descriptions
Ptr<Feature2D> sift = xfeatures2d::SIFT::create();
std::vector<KeyPoint> keypoints1, keypoints2;
Mat descriptors1, descriptors2;
sift->detectAndCompute(grayImg1, Mat( ), keypoints1, descriptors1);
sift->detectAndCompute(grayImg2, Mat(), keypoints2, descriptors2);
// Match feature points
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce");
std::vector<DMatch> matches;
matcher->match(descriptors1, descriptors2, matches);
// 筛选匹配结果
double max_dist = 0, min_dist = 100;
for (int i = 0; i < descriptors1.rows; i++) {
double dist = matches[i].distance;
if (dist < min_dist) min_dist = dist;
if (dist > max_dist) max_dist = dist;
}
std::vector<DMatch> good_matches;
for (int i = 0; i < descriptors1.rows; i++) {
if (matches[i].distance < 3 * min_dist) {
good_matches.push_back(matches[i]);
}
}
// Display matching results
Mat img_matches;
drawMatches(img1, keypoints1, img2, keypoints2, good_matches, img_matches);
imshow("SIFT matches", img_matches);
waitKey(0);
return 0;
}
```
In the above code, we use the `xfeatures2d::SIFT::create()` function to create the SIFT algorithm object, use the `detectAndCompute` function to extract the SIFT feature points and feature descriptions in the two images, and use `DescriptorMatcher::create( "BruteForce")` to create a brute force matching object, use the `match` function to perform feature point matching, and use the `drawMatches` function to visualize the matching results.
It should be noted that in practical applications, the matching results need to be further screened and optimized in order to improve the accuracy and robustness of matching.
SURF algorithm (Speeded Up Robust Features) is a feature detection and description algorithm used in computer vision and image processing. It is an extension of the SIFT algorithm (Scale Invariant Feature Transform), designed to be faster and more robust than SIFT.
The SURF algorithm includes the following steps:
1. Scale-space extremum detection: Similar to SIFT, the first step in SURF is to detect scale-space extrema in the image using the Gaussian difference method. However, SURF uses a faster approximation of the Laplacian of Gaussian (LoG) filter called Box Filter Approximation.
2. Keypoint positioning: Once an extremum is detected, SURF uses the Hessian matrix to determine whether the keypoint is a stable point of interest. This is done by computing the determinant and trace of the Hessian matrix at the extreme positions and comparing it to a threshold.
3. Orientation assignment: SURF calculates the main orientation of each keypoint by computing the Haar wavelet response, which is calculated in the x and y directions around the keypoint location. This produces a set of gradient vectors that are used to compute the orientation histogram. The direction with the highest value in the histogram is used as the main direction.
4. Descriptor creation: SURF creates descriptors for each keypoint by computing the Haar wavelet response in x and y directions within a circular area around the keypoint. Then, transform the response into a rotation-invariant representation by rotating the coordinate system to align with the principal directions.
5. Descriptor matching: SURF uses a modified Euclidean distance to match descriptors. This distance is scale-invariant and robust to changes in illumination and contrast.
The following is the code to implement SURF algorithm using C++ language and OpenCV library:
#include <opencv2/opencv.hpp>
using namespace cv;
int main()
{ // read image Mat img = imread("lena.png");
// Convert to grayscale image
Mat grayImg;
cvtColor(img, grayImg, COLOR_BGR2GRAY);
// Extract SURF feature points
Ptr<Feature2D> surf = xfeatures2d::SURF::create();
std::vector<KeyPoint> keypoints;
surf->detect(grayImg, keypoints);
// Compute SURF feature descriptor
Mat descriptors;
surf->compute(grayImg, keypoints, descriptors);
// Display feature points and descriptors
Mat imgWithKeypoints;
drawKeypoints(grayImg, keypoints, imgWithKeypoints);
imshow("SURF keypoints", imgWithKeypoints);
imshow("SURF descriptors", descriptors);
waitKey(0);
return 0;
}
In the above code, the `imread` function is used to read the image, the `cvtColor` function converts the image into a grayscale image, the `xfeatures2d::SURF::create()` function is used to create the SURF algorithm object, and the `detect` function uses For extracting SURF feature points, the `compute` function is used to calculate the SURF feature descriptor, the `drawKeypoints` function is used to draw the feature points on the image, the `imshow` function is used to display the image, and the `waitKey` function is used to wait for the user to press Press the button.
Image matching using the SURF (Speeded Up Robust Features) algorithm usually has the following steps:
1. Extract the SURF features of the image. For two images, their SURF feature points and descriptors are extracted respectively. It can be implemented using the SURF algorithm in the OpenCV library.
2. Calculate the matching between the feature points of the two images. This can be achieved using the `BFMatcher` or `FlannBasedMatcher` classes in the OpenCV library. The `BFMatcher` class uses a brute-force matching algorithm, while the `FlannBasedMatcher` class uses the Fast Nearest Neighbor (FLANN) algorithm.
3. Select the correct match based on the matching results. You can use RANSAC (Random Sample Consensus) algorithm or other methods to filter out the correct matching points.
4. Plot the matching results. Matches can be drawn on the image using the `drawMatches` function in the OpenCV library.
The following is a code that uses the OpenCV library to implement the SURF algorithm for image matching:
#include <opencv2/opencv.hpp>
using namespace cv;
int main()
{ // read two images Mat img1 = imread("img1.jpg"); Mat img2 = imread("img2.jpg");
// Extract SURF feature points and descriptors
Ptr<Feature2D> surf = xfeatures2d::SURF::create();
std::vector<KeyPoint> keypoints1, keypoints2;
Mat descriptors1, descriptors2;
surf->detectAndCompute(img1, Mat( ), keypoints1, descriptors1);
surf->detectAndCompute(img2, Mat(), keypoints2, descriptors2);
// 匹配特征点
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce");
std::vector<DMatch> matches;
matcher->match(descriptors1, descriptors2, matches);
// 筛选匹配点
double minDist = 100;
double maxDist = 0;
for (int i = 0; i < descriptors1.rows; i++)
{
double dist = matches[i].distance;
if (dist < minDist) minDist = dist;
if (dist > maxDist) maxDist = dist;
}
std::vector<DMatch> goodMatches;
for (int i = 0; i < descriptors1.rows; i++)
{
if (matches[i].distance <= max(2 * minDist, 0.02))
{
goodMatches.push_back(matches[i]);
}
}
// Draw matching results
Mat imgMatches;
drawMatches(img1, keypoints1, img2, keypoints2, goodMatches, imgMatches);
imshow("Matches", imgMatches);
waitKey(0);
return 0;
}
In the above code, the `detectAndCompute` function is used to extract SURF feature points and descriptors at the same time, the `BFMatcher` class is used to implement the brute force matching algorithm, the `match` function is used to perform matching, and the RANSAC algorithm is used to filter out the correct one according to the matching result. Matching points, the `drawMatches` function is used to draw the matching results.