opencv2/xfeatures2d.hpp

#include <opencv2/features.hpp>
#include <opencv2/xfeatures2d/nonfree.hpp>

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Classes

class cv::xfeatures2d::AffineFeature2D

Class implementing affine adaptation for key points.

class cv::xfeatures2d::AgastFeatureDetector

Wrapping class for feature detection using the AGAST method. :

class cv::xfeatures2d::AKAZE

Class implementing the AKAZE keypoint detector and descriptor extractor, described in [12].

class cv::xfeatures2d::BEBLID

Class implementing BEBLID (Boosted Efficient Binary Local Image Descriptor), described in [288] .

class cv::xfeatures2d::BoostDesc

Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in [294] and [295].

class cv::xfeatures2d::BOWImgDescriptorExtractor

Class to compute an image descriptor using the bag of visual words.

class cv::xfeatures2d::BOWKMeansTrainer

kmeans -based class to train visual vocabulary using the bag of visual words approach. :

class cv::xfeatures2d::BOWTrainer

Abstract base class for training the bag of visual words vocabulary from a set of descriptors.

class cv::xfeatures2d::BriefDescriptorExtractor

Class for computing BRIEF descriptors described in [53] .

class cv::xfeatures2d::BRISK

Class implementing the BRISK keypoint detector and descriptor extractor, described in [177] .

class cv::xfeatures2d::DAISY

Class implementing DAISY descriptor, described in [304].

class cv::xfeatures2d::Elliptic_KeyPoint

Elliptic region around an interest point.

class cv::xfeatures2d::FREAK

Class implementing the FREAK (Fast Retina Keypoint) keypoint descriptor, described in [10] .

class cv::xfeatures2d::HarrisLaplaceFeatureDetector

Class implementing the Harris-Laplace feature detector as described in [214].

class cv::xfeatures2d::KAZE

Class implementing the KAZE keypoint detector and descriptor extractor, described in [11] .

class cv::xfeatures2d::LATCH

class cv::xfeatures2d::<a href="../citelist.html#CITEREF_LUCID">[360]</a>

Class implementing the locally uniform comparison image descriptor, described in [360].

class cv::xfeatures2d::MSDDetector

Class implementing the MSD (Maximal Self-Dissimilarity) keypoint detector, described in [305].

class cv::xfeatures2d::PCTSignatures

Class implementing PCT (position-color-texture) signature extraction as described in [171]. 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.

class cv::xfeatures2d::PCTSignaturesSQFD

Class implementing Signature Quadratic Form Distance (SQFD).

class cv::xfeatures2d::StarDetector

The class implements the keypoint detector introduced by [3], synonym of StarDetector. :

class cv::xfeatures2d::TBMR

Class implementing the Tree Based Morse Regions (TBMR) as described in [344] extended with scaled extraction ability.

class cv::xfeatures2d::TEBLID

Class implementing TEBLID (Triplet-based Efficient Binary Local Image Descriptor), described in [289].

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 [275].

Namespaces

namespace cv

namespace cv::xfeatures2d

Functions

void cv::xfeatures2d::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 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 [29] .

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 [195] .