cv::xfeatures2d Namespace Reference

Classes
class	AffineFeature2D
	Class implementing affine adaptation for key points. More...
class	AgastFeatureDetector
	Wrapping class for feature detection using the AGAST method. : More...
class	AKAZE
	Class implementing the AKAZE keypoint detector and descriptor extractor, described in [10]. More...
class	BEBLID
	Class implementing BEBLID (Boosted Efficient Binary Local Image Descriptor), described in [257] . More...
class	BoostDesc
	Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in [262] and [263]. More...
class	BOWImgDescriptorExtractor
	Class to compute an image descriptor using the bag of visual words. More...
class	BOWKMeansTrainer
	kmeans -based class to train visual vocabulary using the bag of visual words approach. : More...
class	BOWTrainer
	Abstract base class for training the bag of visual words vocabulary from a set of descriptors. More...
class	BriefDescriptorExtractor
	Class for computing BRIEF descriptors described in [47] . More...
class	BRISK
	Class implementing the BRISK keypoint detector and descriptor extractor, described in [159] . More...
class	DAISY
	Class implementing DAISY descriptor, described in [271]. More...
class	Elliptic_KeyPoint
	Elliptic region around an interest point. More...
class	FREAK
	Class implementing the FREAK (Fast Retina Keypoint) keypoint descriptor, described in [8] . More...
class	HarrisLaplaceFeatureDetector
	Class implementing the Harris-Laplace feature detector as described in [193]. More...
class	KAZE
	Class implementing the KAZE keypoint detector and descriptor extractor, described in [9] . More...
class	LATCH
class	LUCID
	Class implementing the locally uniform comparison image descriptor, described in [322]. More...
class	MSDDetector
	Class implementing the MSD (Maximal Self-Dissimilarity) keypoint detector, described in [272]. More...
class	PCTSignatures
	Class implementing PCT (position-color-texture) signature extraction as described in [152]. 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	PCTSignaturesSQFD
	Class implementing Signature Quadratic Form Distance (SQFD). More...
class	StarDetector
	The class implements the keypoint detector introduced by [2], synonym of StarDetector. : More...
class	SURF
	Class for extracting Speeded Up Robust Features from an image [20] . More...
class	TBMR
	Class implementing the Tree Based Morse Regions (TBMR) as described in [308] extended with scaled extraction ability. More...
class	TEBLID
	Class implementing TEBLID (Triplet-based Efficient Binary Local Image Descriptor), described in [258]. More...
class	VGG
	Class implementing VGG (Oxford Visual Geometry Group) descriptor trained end to end using "Descriptor Learning Using Convex Optimisation" (DLCO) aparatus described in [247]. More...

Typedefs
typedef SURF	SurfDescriptorExtractor
typedef SURF	SurfFeatureDetector

Functions
void	AGAST (InputArray image, std::vector< KeyPoint > &keypoints, int threshold, bool nonmaxSuppression=true, AgastFeatureDetector::DetectorType type=AgastFeatureDetector::OAST_9_16)
	Detects corners using the AGAST algorithm.
void	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.
void	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] .
void	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 [175] .