Class cv::xfeatures2d::BEBLID#

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

#include <opencv2/xfeatures2d.hpp>

Collaboration diagram for cv::xfeatures2d::BEBLID:

Public Types#

Descriptor number of bits, each bit is a boosting weak-learner. The user can choose between 512 or 256 bits.

enum BeblidSize {
    SIZE_512_BITS = 100,
    SIZE_256_BITS = 101
}

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 BEBLID (Boosted Efficient Binary Local Image Descriptor), described in Suarez2020BEBLID .

BEBLID Suarez2020BEBLID is a efficient binary descriptor learned with boosting. It is able to describe keypoints from any detector just by changing the scale_factor parameter. In several benchmarks it has proved to largely improve other binary descriptors like ORB or BRISK with the same efficiency. BEBLID describes using the difference of mean gray values in different regions of the image around the KeyPoint, the descriptor is specifically optimized for image matching and patch retrieval addressing the asymmetries of these problems.

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

Iago Suárez, Ghesn Sfeir, José M. Buenaposada, and Luis Baumela. BEBLID: Boosted efficient binary local image descriptor. Pattern Recognition Letters, 133:366–372, 2020.

The descriptor was trained using 1 million of randomly sampled pairs of patches (20% positives and 80% negatives) from the Liberty split of the UBC datasets winder2007learning as described in the paper Suarez2020BEBLID. You can check in the AKAZE example how well BEBLID works. Detecting 10000 keypoints with ORB and describing with BEBLID obtains 561 inliers (75%) whereas describing with ORB obtains only 493 inliers (63%).

Member Enumeration Documentation#

enum BeblidSize

Descriptor number of bits, each bit is a boosting weak-learner. The user can choose between 512 or 256 bits.

SIZE_512_BITS

SIZE_256_BITS

Member Function Documentation#

create()#

static Ptr< BEBLID > cv::xfeatures2d::BEBLID::create(
float scale_factor,
int n_bits = BEBLID::SIZE_512_BITS )

Creates the BEBLID 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 BEBLID::SIZE_512_BITS or BEBLID::SIZE_256_BITS.

getDefaultName()#

String cv::xfeatures2d::BEBLID::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.

getScaleFactor()#

float cv::xfeatures2d::BEBLID::getScaleFactor()

setScaleFactor()#

void cv::xfeatures2d::BEBLID::setScaleFactor(float scale_factor)

Source file#

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

opencv2/xfeatures2d.hpp