全面綜述：圖像特征提取與匹配技術

int nfeatures = 0;// The number of best features to retain.int nOctaveLayers = 3;// The number of layers in each octave. 3 is the value used in D. Lowe paper.double contrastThreshold = 0.04;// The contrast threshold used to filter out weak features in semi-uniform (low-contrast) regions.        double edgeThreshold = 10;// The threshold used to filter out edge-like features.        double sigma = 1.6;        // The sigma of the Gaussian applied to the input image at the octave \#0.xxx=cv::xfeatures2d::SIFT::create(nfeatures, nOctaveLayers, contrastThreshold, edgeThreshold, sigma);

// Detector parametersint blockSize = 2;     // for every pixel, a blockSize × blockSize neighborhood is consideredint apertureSize = 3;  // aperture parameter for Sobel operator (must be odd)int minResponse = 100; // minimum value for a corner in the 8bit scaled response matrixdouble k = 0.04;       // Harris parameter (see equation for details)// Detect Harris corners and normalize outputcv::Mat dst, dst_norm, dst_norm_scaled;dst = cv::Mat::zeros(img.size(), CV_32FC1);cv::cornerHarris(img, dst, blockSize, apertureSize, k, cv::BORDER_DEFAULT);cv::normalize(dst, dst_norm, 0, 255, cv::NORM_MINMAX, CV_32FC1, cv::Mat());cv::convertScaleAbs(dst_norm, dst_norm_scaled);
// Look for prominent corners and instantiate keypointsdouble maxOverlap = 0.0; // max. permissible overlap between two features in %, used during non-maxima suppressionfor (size_t j = 0; j < dst_norm.rows; j++) {    for (size_t i = 0; i < dst_norm.cols; i++) {        int response = (int) dst_norm.at(j, i);        if (response > minResponse) { // only store points above a threshold
            cv::KeyPoint newKeyPoint;            newKeyPoint.pt = cv::Point2f(i, j);            newKeyPoint.size = 2 * apertureSize;            newKeyPoint.response = response;
      // perform non-maximum suppression (NMS) in local neighbourhood around new key point            bool bOverlap = false;            for (auto it = keypoints.begin(); it != keypoints.end(); ++it) {                double kptOverlap = cv::KeyPoint::overlap(newKeyPoint, *it);                if (kptOverlap > maxOverlap) {                    bOverlap = true;                    if (newKeyPoint.response >                        (*it).response) {                                              // if overlap is >t AND response is higher for new kpt                        *it = newKeyPoint; // replace old key point with new one                        break;             // quit loop over keypoints                    }                }            }            if (!bOverlap) {                                                 // only add new key point if no overlap has been found in previous NMS                keypoints.push_back(newKeyPoint); // store new keypoint in dynamic list            }        }    } // eof loop over cols}     // eof loop over rows

int blockSize = 6;       //  size of an average block for computing a derivative covariation matrix over each pixel neighborhooddouble maxOverlap = 0.0; // max. permissible overlap between two features in %double minDistance = (1.0 - maxOverlap) * blockSize;int maxCorners = img.rows * img.cols / max(1.0, minDistance); // max. num. of keypointsdouble qualityLevel = 0.01; // minimal accepted quality of image cornersdouble k = 0.04;bool useHarris = false;// Apply corner detectionvector corners;cv::goodFeaturesToTrack(img, corners, maxCorners, qualityLevel, minDistance, cv::Mat(), blockSize, useHarris, k);
// add corners to result vectorfor (auto it = corners.begin(); it != corners.end(); ++it) {    cv::KeyPoint newKeyPoint;    newKeyPoint.pt = cv::Point2f((*it).x, (*it).y);    newKeyPoint.size = blockSize;    keypoints.push_back(newKeyPoint);}

int threshold = 30;        // FAST/AGAST detection threshold score.int octaves = 3;           // detection octaves (use 0 to do single scale)float patternScale = 1.0f; // apply this scale to the pattern used for sampling the neighbourhood of a keypoint.xxx=cv::BRISK::create(threshold, octaves, patternScale);

bool orientationNormalized = true;// Enable orientation normalization.bool scaleNormalized = true;// Enable scale normalization.float patternScale = 22.0f;// Scaling of the description pattern.int nOctaves = 4;// Number of octaves covered by the detected keypoints.const std::vector &selectedPairs = std::vector(); // (Optional) user defined selected pairs indexes,xxx=cv::xfeatures2d::FREAK::create(orientationNormalized, scaleNormalized, patternScale, nOctaves,selectedPairs);

int threshold = 30;// Difference between intensity of the central pixel and pixels of a circle around this pixelbool nonmaxSuppression = true;// perform non-maxima suppression on keypointscv::FastFeatureDetector::DetectorType type = cv::FastFeatureDetector::TYPE_9_16;// TYPE_9_16, TYPE_7_12, TYPE_5_8xxx=cv::FastFeatureDetector::create(threshold, nonmaxSuppression, type);

int nfeatures = 500;// The maximum number of features to retain.float scaleFactor = 1.2f;// Pyramid decimation ratio, greater than 1.int nlevels = 8;// The number of pyramid levels.int edgeThreshold = 31;// This is size of the border where the features are not detected.int firstLevel = 0;// The level of pyramid to put source image to.int WTA_K = 2;// The number of points that produce each element of the oriented BRIEF descriptor.auto scoreType = cv::ORB::HARRIS_SCORE;// The default HARRIS_SCORE means that Harris algorithm is used to rank features.int patchSize = 31;// Size of the patch used by the oriented BRIEF descriptor.int fastThreshold = 20;// The fast threshold.xxx=cv::ORB::create(nfeatures, scaleFactor, nlevels, edgeThreshold, firstLevel, WTA_K, scoreType,patchSize, fastThreshold);

auto descriptor_type = cv::AKAZE::DESCRIPTOR_MLDB;// Type of the extracted descriptor: DESCRIPTOR_KAZE, DESCRIPTOR_KAZE_UPRIGHT, DESCRIPTOR_MLDB or DESCRIPTOR_MLDB_UPRIGHT.int descriptor_size = 0;// Size of the descriptor in bits. 0 -> Full sizeint descriptor_channels = 3;// Number of channels in the descriptor (1, 2, 3)float threshold = 0.001f;// Detector response threshold to accept pointint nOctaves = 4;// Maximum octave evolution of the imageint nOctaveLayers = 4;// Default number of sublevels per scale levelauto diffusivity = cv::KAZE::DIFF_PM_G2;// Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or DIFF_CHARBONNIERxxx=cv::AKAZE::create(descriptor_type, descriptor_size, descriptor_channels, threshold, nOctaves,nOctaveLayers, diffusivity);

int bytes = 32;// Legth of the descriptor in bytes, valid values are: 16, 32 (default) or 64 .bool use_orientation = false;// Sample patterns using keypoints orientation, disabled by default.xxx=cv::xfeatures2d::BriefDescriptorExtractor::create(bytes, use_orientation);

void matchDescriptors(std::vector &kPtsSource, std::vector &kPtsRef, cv::Mat &descSource,cv::Mat &descRef,std::vector &matches, std::string descriptorclass, std::string matcherType,std::string selectorType) {    // configure matcher    bool crossCheck = false;    cv::Ptr matcher;    int normType;
    if (matcherType.compare("MAT_BF") == 0) {        int normType = descriptorclass.compare("DES_BINARY") == 0 ? cv::NORM_HAMMING : cv::NORM_L2;        matcher = cv::BFMatcher::create(normType, crossCheck);
    } else if (matcherType.compare("MAT_FLANN") == 0) {        // OpenCV bug workaround : convert binary descriptors to floating point due to a bug in current OpenCV implementation        if (descSource.type() !=CV_32F) {            descSource.convertTo(descSource, CV_32F);            // descRef.convertTo(descRef, CV_32F);        }        if (descRef.type() !=CV_32F) {            descRef.convertTo(descRef, CV_32F);        }        matcher = cv::DescriptorMatcher::create(cv::DescriptorMatcher::FLANNBASED);    }
    // perform matching task    if (selectorType.compare("SEL_NN") == 0) { // nearest neighbor (best match)        matcher->match(descSource, descRef, matches);         // Finds the best match for each descriptor in desc1    } else if (selectorType.compare("SEL_KNN") == 0) { // k nearest neighbors (k=2)        vector> knn_matches;        matcher->knnMatch(descSource, descRef, knn_matches, 2);        //-- Filter matches using the Lowe's ratio test        double minDescDistRatio = 0.8;        for (auto it = knn_matches.begin(); it != knn_matches.end(); ++it) {            if ((*it)[0].distance < minDescDistRatio * (*it)[1].distance) {                matches.push_back((*it)[0]);            }        }    } }