Class cv::xfeatures2d::TEBLID#

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

#include <opencv2/xfeatures2d.hpp>

Collaboration diagram for cv::xfeatures2d::TEBLID:

Public Types#

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

enum TeblidSize {
    SIZE_256_BITS = 102,
    SIZE_512_BITS = 103
}

Public Member Functions#

Public Member Functions inherited from cv::Feature2D

Return	Name	Description
	`~Feature2D()`
`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).
`void`	`compute(` `InputArrayOfArrays images,` `std::vector< std::vector< KeyPoint > > & keypoints,` `OutputArrayOfArrays descriptors )`
`int`	`defaultNorm()`
`int`	`descriptorSize()`
`int`	`descriptorType()`
`void`	`detect(` `InputArray image,` `std::vector< KeyPoint > & keypoints,` `InputArray mask = noArray() )`	Detects keypoints in an image (first variant) or image set (second variant).
`void`	`detect(` `InputArrayOfArrays images,` `std::vector< std::vector< KeyPoint > > & keypoints,` `InputArrayOfArrays masks = noArray() )`
`void`	`detectAndCompute(` `InputArray image,` `InputArray mask,` `std::vector< KeyPoint > & keypoints,` `OutputArray descriptors,` `bool useProvidedKeypoints = false )`
`bool`	`empty()`	Return true if detector object is empty.
`String`	`getDefaultName()`
`void`	`read(const FileNode & fn)`	Reads algorithm parameters from a file storage.
`void`	`read(const String & fileName)`
`void`	`write(const String & fileName)`
`void`	`write(FileStorage & fs)`	Stores algorithm parameters in a file storage.
`void`	`write(` `FileStorage & fs,` `const String & name )`

Public Member Functions inherited from cv::Algorithm

Return	Name	Description
	`Algorithm()`
	`~Algorithm()`
`void`	`clear()`	Clears the algorithm state.
`bool`	`empty()`	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
`String`	`getDefaultName()`
`void`	`read(const FileNode & fn)`	Reads algorithm parameters from a file storage.
`void`	`save(const String & filename)`
`void`	`write(` `const Ptr< FileStorage > & fs,` `const String & name = String() )`
`void`	`write(FileStorage & fs)`	Stores algorithm parameters in a file storage.
`void`	`write(` `FileStorage & fs,` `const String & name )`

Static Public Member Functions#

Static Public Member Functions inherited from cv::Algorithm

Return	Name	Description
`static Ptr< _Tp >`	`load(` `const String & filename,` `const String & objname = String() )`	Loads algorithm from the file.
`static Ptr< _Tp >`	`loadFromString(` `const String & strModel,` `const String & objname = String() )`	Loads algorithm from a String.
`static Ptr< _Tp >`	`read(const FileNode & fn)`	Reads algorithm from the file node.

Additional Inherited Members#

Detailed Description#

Class implementing TEBLID (Triplet-based Efficient Binary Local Image Descriptor), described in Suarez2021TEBLID.

TEBLID stands for Triplet-based Efficient Binary Local Image Descriptor, although originally it was called BAD Suarez2021TEBLID. It is an improvement over BEBLID Suarez2020BEBLID, 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 winder2007learning .

Member Enumeration Documentation#

enum TeblidSize

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

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 )

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()

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.

Source file#

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

opencv2/xfeatures2d.hpp