xfeatures2d.hpp File Reference

#include "opencv2/features2d.hpp"
#include "opencv2/xfeatures2d/nonfree.hpp"

Classes
class	cv::xfeatures2d::AffineFeature2D
	Class implementing affine adaptation for key points. More...
class	cv::xfeatures2d::BEBLID
	Class implementing BEBLID (Boosted Efficient Binary Local Image Descriptor), described in [251] . More...
class	cv::xfeatures2d::BoostDesc
	Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in [256] and [257]. More...
class	cv::xfeatures2d::BriefDescriptorExtractor
	Class for computing BRIEF descriptors described in [47] . More...
class	cv::xfeatures2d::DAISY
	Class implementing DAISY descriptor, described in [265]. 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 [8] . More...
class	cv::xfeatures2d::HarrisLaplaceFeatureDetector
	Class implementing the Harris-Laplace feature detector as described in [188]. More...
class	cv::xfeatures2d::LATCH
class	cv::xfeatures2d::LUCID
	Class implementing the locally uniform comparison image descriptor, described in [316]. More...
class	cv::xfeatures2d::MSDDetector
	Class implementing the MSD (Maximal Self-Dissimilarity) keypoint detector, described in [266]. More...
class	cv::xfeatures2d::PCTSignatures
	Class implementing PCT (position-color-texture) signature extraction as described in [147]. 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::TBMR
	Class implementing the Tree Based Morse Regions (TBMR) as described in [302] extended with scaled extraction ability. More...
class	cv::xfeatures2d::TEBLID
	Class implementing TEBLID (Triplet-based Efficient Binary Local Image Descriptor), described in [252]. 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 [241]. More...

Namespaces
	cv
	"black box" representation of the file storage associated with a file on disk.
	cv::xfeatures2d

Functions
void	cv::xfeatures2d::FASTForPointSet (InputArray image, std::vector< KeyPoint > &keypoints, int threshold, bool nonmaxSuppression=true, cv::FastFeatureDetector::DetectorType 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 described in [26] . More...
void	cv::xfeatures2d::matchLOGOS (const std::vector< KeyPoint > &keypoints1, const std::vector< KeyPoint > &keypoints2, const std::vector< int > &nn1, const std::vector< int > &nn2, std::vector< DMatch > &matches1to2)
	LOGOS (Local geometric support for high-outlier spatial verification) feature matching strategy described in [170] . More...