Class implementing TEBLID (Triplet-based Efficient Binary Local Image Descriptor), described in [257]. More...

#include <opencv2/xfeatures2d.hpp>

Collaboration diagram for cv::xfeatures2d::TEBLID:

Public Types
enum	TeblidSize { SIZE_256_BITS = 102 , SIZE_512_BITS = 103 }
	Descriptor number of bits, each bit is a box average difference. The user can choose between 256 or 512 bits. More...

Public Member Functions
String	getDefaultName () const CV_OVERRIDE

Public Member Functions inherited from cv::Feature2D
virtual	~Feature2D ()

virtual void	compute (InputArray image, std::vector< KeyPoint > &keypoints, OutputArray descriptors)
	Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant).

virtual void	compute (InputArrayOfArrays images, std::vector< std::vector< KeyPoint > > &keypoints, OutputArrayOfArrays descriptors)

virtual int	defaultNorm () const

virtual int	descriptorSize () const

virtual int	descriptorType () const

virtual void	detect (InputArray image, std::vector< KeyPoint > &keypoints, InputArray mask=noArray())
	Detects keypoints in an image (first variant) or image set (second variant).

virtual void	detect (InputArrayOfArrays images, std::vector< std::vector< KeyPoint > > &keypoints, InputArrayOfArrays masks=noArray())

virtual void	detectAndCompute (InputArray image, InputArray mask, std::vector< KeyPoint > &keypoints, OutputArray descriptors, bool useProvidedKeypoints=false)

virtual bool	empty () const CV_OVERRIDE
	Return true if detector object is empty.

virtual void	read (const FileNode &) CV_OVERRIDE
	Reads algorithm parameters from a file storage.

void	read (const String &fileName)

void	write (const Ptr< FileStorage > &fs, const String &name) const

void	write (const String &fileName) const

virtual void	write (FileStorage &) const CV_OVERRIDE
	Stores algorithm parameters in a file storage.

void	write (FileStorage &fs, const String &name) const

Public Member Functions inherited from cv::Algorithm
	Algorithm ()

virtual	~Algorithm ()

virtual void	clear ()
	Clears the algorithm state.

virtual void	save (const String &filename) const

void	write (const Ptr< FileStorage > &fs, const String &name=String()) const

void	write (FileStorage &fs, const String &name) const

Static Public Member Functions
static Ptr< TEBLID >	create (float scale_factor, int n_bits=TEBLID::SIZE_256_BITS)
	Creates the TEBLID descriptor.

Static Public Member Functions inherited from cv::Algorithm
template<typename _Tp >
static Ptr< _Tp >	load (const String &filename, const String &objname=String())
	Loads algorithm from the file.

template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String.

template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node.

Additional Inherited Members
Protected Member Functions inherited from cv::Algorithm
void	writeFormat (FileStorage &fs) const

Detailed Description

Class implementing TEBLID (Triplet-based Efficient Binary Local Image Descriptor), described in [257].

TEBLID stands for Triplet-based Efficient Binary Local Image Descriptor, although originally it was called BAD [257]. It is an improvement over BEBLID [256], that uses triplet loss, hard negative mining, and anchor swap to improve the image matching results. It is able to describe keypoints from any detector just by changing the scale_factor parameter. TEBLID is as efficient as ORB, BEBLID or BRISK, but the triplet-based training objective selected more discriminative features that explain the accuracy gain. It is also more compact than BEBLID, when running the AKAZE example with 10000 keypoints detected by ORB, BEBLID obtains 561 inliers (75%) with 512 bits, whereas TEBLID obtains 621 (75.2%) with 256 bits. ORB obtains only 493 inliers (63%).

If you find this code useful, please add a reference to the following paper:

Iago Suárez, José M. Buenaposada, and Luis Baumela. Revisiting Binary Local Image Description for Resource Limited Devices. IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 8317-8324, Oct. 2021.

The descriptor was trained in Liberty split of the UBC datasets [298] .

Member Enumeration Documentation

◆ TeblidSize

enum cv::xfeatures2d::TEBLID::TeblidSize

Descriptor number of bits, each bit is a box average difference. The user can choose between 256 or 512 bits.

Enumerator
SIZE_256_BITS
SIZE_512_BITS

Member Function Documentation

◆ create()

static Ptr< TEBLID > cv::xfeatures2d::TEBLID::create	(	float	scale_factor,
		int	n_bits = `TEBLID::SIZE_256_BITS`
	)

static

Python:
	cv.xfeatures2d.TEBLID.create(	scale_factor[, n_bits]	) ->	retval
	cv.xfeatures2d.TEBLID_create(	scale_factor[, n_bits]	) ->	retval

Creates the TEBLID descriptor.

Parameters

scale_factor	Adjust the sampling window around detected keypoints: 1.00f should be the scale for ORB keypoints 6.75f should be the scale for SIFT detected keypoints 6.25f is default and fits for KAZE, SURF detected keypoints 5.00f should be the scale for AKAZE, MSD, AGAST, FAST, BRISK keypoints
n_bits	Determine the number of bits in the descriptor. Should be either TEBLID::SIZE_256_BITS or TEBLID::SIZE_512_BITS.

◆ getDefaultName()

String cv::xfeatures2d::TEBLID::getDefaultName ( ) const

virtual

Python:
	cv.xfeatures2d.TEBLID.getDefaultName(		) ->	retval

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

Reimplemented from cv::Feature2D.

The documentation for this class was generated from the following file:

opencv2/xfeatures2d.hpp

Public Types

Public Member Functions