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xfeatures2d.hpp File Reference

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 [Suarez2020BEBLID] . More...
 
class  cv::xfeatures2d::BoostDesc
 Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in [Trzcinski13a] and [Trzcinski13b]. More...
 
class  cv::xfeatures2d::BriefDescriptorExtractor
 Class for computing BRIEF descriptors described in [calon2010] . More...
 
class  cv::xfeatures2d::DAISY
 Class implementing DAISY descriptor, described in [Tola10]. 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 [AOV12] . More...
 
class  cv::xfeatures2d::HarrisLaplaceFeatureDetector
 Class implementing the Harris-Laplace feature detector as described in [Mikolajczyk2004]. More...
 
class  cv::xfeatures2d::LATCH
 
class  cv::xfeatures2d::LUCID
 Class implementing the locally uniform comparison image descriptor, described in [LUCID]. More...
 
class  cv::xfeatures2d::MSDDetector
 Class implementing the MSD (Maximal Self-Dissimilarity) keypoint detector, described in [Tombari14]. More...
 
class  cv::xfeatures2d::PCTSignatures
 Class implementing PCT (position-color-texture) signature extraction as described in [KrulisLS16]. 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 [Agrawal08], synonym of StarDetector. : More...
 
class  cv::xfeatures2d::TBMR
 Class implementing the Tree Based Morse Regions (TBMR) as described in [Najman2014] extended with scaled extraction ability. More...
 
class  cv::xfeatures2d::TEBLID
 Class implementing TEBLID (Triplet-based Efficient Binary Local Image Descriptor), described in [Suarez2021TEBLID]. 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 [Simonyan14]. More...
 

Namespaces

namespace  cv
 "black box" representation of the file storage associated with a file on disk.
 
namespace  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.
 
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 [Bian2017gms] .
 
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 [Lowry2018LOGOSLG] .