Experimental 2D Features Algorithms

Detailed Description

This section describes experimental algorithms for 2d feature detection.

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 [256] . More...
class	cv::xfeatures2d::BoostDesc
	Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in [261] and [262]. More...
class	cv::xfeatures2d::BriefDescriptorExtractor
	Class for computing BRIEF descriptors described in [47] . More...
class	cv::xfeatures2d::DAISY
	Class implementing DAISY descriptor, described in [270]. 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 [193]. More...
class	cv::xfeatures2d::LATCH
class	cv::xfeatures2d::LUCID
	Class implementing the locally uniform comparison image descriptor, described in [321]. More...
class	cv::xfeatures2d::MSDDetector
	Class implementing the MSD (Maximal Self-Dissimilarity) keypoint detector, described in [271]. More...
class	cv::xfeatures2d::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	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 [307] extended with scaled extraction ability. More...
class	cv::xfeatures2d::TEBLID
	Class implementing TEBLID (Triplet-based Efficient Binary Local Image Descriptor), described in [257]. 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 [246]. More...

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.

Function Documentation

FASTForPointSet()

void cv::xfeatures2d::FASTForPointSet	(	InputArray	image,
		std::vector< KeyPoint > &	keypoints,
		int	threshold,
		bool	nonmaxSuppression = true,
		cv::FastFeatureDetector::DetectorType	type = FastFeatureDetector::TYPE_9_16 )

#include <opencv2/xfeatures2d.hpp>

Estimates cornerness for prespecified KeyPoints using the FAST algorithm.

Parameters

image	grayscale image where keypoints (corners) are detected.
keypoints	keypoints which should be tested to fit the FAST criteria. Keypoints not being detected as corners are removed.
threshold	threshold on difference between intensity of the central pixel and pixels of a circle around this pixel.
nonmaxSuppression	if true, non-maximum suppression is applied to detected corners (keypoints).
type	one of the three neighborhoods as defined in the paper: FastFeatureDetector::TYPE_9_16, FastFeatureDetector::TYPE_7_12, FastFeatureDetector::TYPE_5_8

Detects corners using the FAST algorithm by [228] .