|  | OpenCV
    3.4.9
    Open Source Computer Vision | 
| Classes | |
| class | cv::xfeatures2d::AffineFeature2D | 
| Class implementing affine adaptation for key points.  More... | |
| class | cv::xfeatures2d::BoostDesc | 
| Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in [196] and [197].  More... | |
| class | cv::xfeatures2d::BriefDescriptorExtractor | 
| Class for computing BRIEF descriptors described in [33] .  More... | |
| class | cv::xfeatures2d::DAISY | 
| Class implementing DAISY descriptor, described in [203].  More... | |
| class | cv::xfeatures2d::Elliptic_KeyPoint | 
| Elliptic region around an interest point.  More... | |
| class | cv::xfeatures2d::FREAK | 
| Class implementing the FREAK (Fast Retina Keypoint) keypoint descriptor, described in [4] .  More... | |
| class | cv::xfeatures2d::HarrisLaplaceFeatureDetector | 
| Class implementing the Harris-Laplace feature detector as described in [149].  More... | |
| class | cv::xfeatures2d::LATCH | 
| class | cv::xfeatures2d::LUCID | 
| Class implementing the locally uniform comparison image descriptor, described in [245].  More... | |
| class | cv::xfeatures2d::MSDDetector | 
| Class implementing the MSD (Maximal Self-Dissimilarity) keypoint detector, described in [204].  More... | |
| class | cv::xfeatures2d::PCTSignatures | 
| Class implementing PCT (position-color-texture) signature extraction as described in [114]. The algorithm is divided to a feature sampler and a clusterizer. Feature sampler produces samples at given set of coordinates. Clusterizer then produces clusters of these samples using k-means algorithm. Resulting set of clusters is the signature of the input image.  More... | |
| class | cv::xfeatures2d::PCTSignaturesSQFD | 
| Class implementing Signature Quadratic Form Distance (SQFD).  More... | |
| class | cv::xfeatures2d::StarDetector | 
| The class implements the keypoint detector introduced by [2], synonym of StarDetector. :  More... | |
| class | cv::xfeatures2d::VGG | 
| Class implementing VGG (Oxford Visual Geometry Group) descriptor trained end to end using "Descriptor Learning Using Convex Optimisation" (DLCO) aparatus described in [187].  More... | |
| Namespaces | |
| cv | |
| cv::xfeatures2d | |
| Functions | |
| void | cv::xfeatures2d::FASTForPointSet (InputArray image, std::vector< KeyPoint > &keypoints, int threshold, bool nonmaxSuppression=true, int type=FastFeatureDetector::TYPE_9_16) | 
| Estimates cornerness for prespecified KeyPoints using the FAST algorithm.  More... | |
| void | cv::xfeatures2d::matchGMS (const Size &size1, const Size &size2, const std::vector< KeyPoint > &keypoints1, const std::vector< KeyPoint > &keypoints2, const std::vector< DMatch > &matches1to2, std::vector< DMatch > &matchesGMS, const bool withRotation=false, const bool withScale=false, const double thresholdFactor=6.0) | 
| GMS (Grid-based Motion Statistics) feature matching strategy by [17] .  More... | |
 1.8.13
 1.8.13