cv::line_descriptor::BinaryDescriptorMatcher Class Reference

furnishes all functionalities for querying a dataset provided by user or internal to class (that user must, anyway, populate) on the model of Descriptor Matchers More...

#include <opencv2/line_descriptor/descriptor.hpp>

Collaboration diagram for cv::line_descriptor::BinaryDescriptorMatcher:

Public Member Functions
	BinaryDescriptorMatcher ()
	Constructor.
	~BinaryDescriptorMatcher ()
void	add (const std::vector< Mat > &descriptors)
	Store locally new descriptors to be inserted in dataset, without updating dataset.
void	clear () CV_OVERRIDE
	Clear dataset and internal data.
void	knnMatch (const Mat &queryDescriptors, const Mat &trainDescriptors, std::vector< std::vector< DMatch > > &matches, int k, const Mat &mask=Mat(), bool compactResult=false) const
	For every input query descriptor, retrieve the best k matching ones from a dataset provided from user or from the one internal to class.
void	knnMatch (const Mat &queryDescriptors, std::vector< std::vector< DMatch > > &matches, int k, const std::vector< Mat > &masks=std::vector< Mat >(), bool compactResult=false)
void	match (const Mat &queryDescriptors, const Mat &trainDescriptors, std::vector< DMatch > &matches, const Mat &mask=Mat()) const
	For every input query descriptor, retrieve the best matching one from a dataset provided from user or from the one internal to class.
void	match (const Mat &queryDescriptors, std::vector< DMatch > &matches, const std::vector< Mat > &masks=std::vector< Mat >())
void	radiusMatch (const Mat &queryDescriptors, const Mat &trainDescriptors, std::vector< std::vector< DMatch > > &matches, float maxDistance, const Mat &mask=Mat(), bool compactResult=false) const
	For every input query descriptor, retrieve, from a dataset provided from user or from the one internal to class, all the descriptors that are not further than maxDist from input query.
void	radiusMatch (const Mat &queryDescriptors, std::vector< std::vector< DMatch > > &matches, float maxDistance, const std::vector< Mat > &masks=std::vector< Mat >(), bool compactResult=false)
void	train ()
	Update dataset by inserting into it all descriptors that were stored locally by add function.
Public Member Functions inherited from cv::Algorithm
	Algorithm ()
virtual	~Algorithm ()
virtual bool	empty () const
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
virtual String	getDefaultName () const
virtual void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage.
virtual void	save (const String &filename) const
virtual void	write (FileStorage &fs) const
	Stores algorithm parameters in a file storage.
void	write (FileStorage &fs, const String &name) const

Static Public Member Functions
static Ptr< BinaryDescriptorMatcher >	createBinaryDescriptorMatcher ()
	Create a BinaryDescriptorMatcher object and return a smart pointer to it.
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

furnishes all functionalities for querying a dataset provided by user or internal to class (that user must, anyway, populate) on the model of Descriptor Matchers

Once descriptors have been extracted from an image (both they represent lines and points), it becomes interesting to be able to match a descriptor with another one extracted from a different image and representing the same line or point, seen from a differente perspective or on a different scale. In reaching such goal, the main headache is designing an efficient search algorithm to associate a query descriptor to one extracted from a dataset. In the following, a matching modality based on Multi-Index Hashing (MiHashing) will be described.

Multi-Index Hashing

The theory described in this section is based on [210] . Given a dataset populated with binary codes, each code is indexed m times into m different hash tables, according to m substrings it has been divided into. Thus, given a query code, all the entries close to it at least in one substring are returned by search as neighbor candidates. Returned entries are then checked for validity by verifying that their full codes are not distant (in Hamming space) more than r bits from query code. In details, each binary code h composed of b bits is divided into m disjoint substrings \(\mathbf{h}^{(1)}, ..., \mathbf{h}^{(m)}\), each with length \(\lfloor b/m \rfloor\) or \(\lceil b/m \rceil\) bits. Formally, when two codes h and g differ by at the most r bits, in at the least one of their m substrings they differ by at the most \(\lfloor r/m \rfloor\) bits. In particular, when \(||\mathbf{h}-\mathbf{g}||_H \le r\) (where \(||.||_H\) is the Hamming norm), there must exist a substring k (with \(1 \le k \le m\)) such that

\[||\mathbf{h}^{(k)} - \mathbf{g}^{(k)}||_H \le \left\lfloor \frac{r}{m} \right\rfloor .\]

That means that if Hamming distance between each of the m substring is strictly greater than \(\lfloor r/m \rfloor\), then \(||\mathbf{h}-\mathbf{g}||_H\) must be larger that r and that is a contradiction. If the codes in dataset are divided into m substrings, then m tables will be built. Given a query q with substrings \(\{\mathbf{q}^{(i)}\}^m_{i=1}\), i-th hash table is searched for entries distant at the most \(\lfloor r/m \rfloor\) from \(\mathbf{q}^{(i)}\) and a set of candidates \(\mathcal{N}_i(\mathbf{q})\) is obtained. The union of sets \(\mathcal{N}(\mathbf{q}) = \bigcup_i \mathcal{N}_i(\mathbf{q})\) is a superset of the r-neighbors of q. Then, last step of algorithm is computing the Hamming distance between q and each element in \(\mathcal{N}(\mathbf{q})\), deleting the codes that are distant more that r from q.

Constructor & Destructor Documentation

BinaryDescriptorMatcher()

cv::line_descriptor::BinaryDescriptorMatcher::BinaryDescriptorMatcher

(

)

Python:
	cv.line_descriptor.BinaryDescriptorMatcher(		) ->	<line_descriptor_BinaryDescriptorMatcher object>

Constructor.

The BinaryDescriptorMatcher constructed is able to store and manage 256-bits long entries.

~BinaryDescriptorMatcher()

cv::line_descriptor::BinaryDescriptorMatcher::~BinaryDescriptorMatcher ( )

inline

destructor

Member Function Documentation

add()

void cv::line_descriptor::BinaryDescriptorMatcher::add

(

const std::vector< Mat > &

descriptors

)

Store locally new descriptors to be inserted in dataset, without updating dataset.

Parameters

descriptors

matrices containing descriptors to be inserted into dataset

Note

Each matrix i in descriptors should contain descriptors relative to lines extracted from i*-th image.

clear()

void cv::line_descriptor::BinaryDescriptorMatcher::clear ( )

virtual

Clear dataset and internal data.

Reimplemented from cv::Algorithm.

createBinaryDescriptorMatcher()

static Ptr< BinaryDescriptorMatcher > cv::line_descriptor::BinaryDescriptorMatcher::createBinaryDescriptorMatcher ( )

static

Create a BinaryDescriptorMatcher object and return a smart pointer to it.

knnMatch() [1/2]

void cv::line_descriptor::BinaryDescriptorMatcher::knnMatch	(	const Mat &	queryDescriptors,
		const Mat &	trainDescriptors,
		std::vector< std::vector< DMatch > > &	matches,
		int	k,
		const Mat &	mask = Mat(),
		bool	compactResult = false ) const

Python:
	cv.line_descriptor.BinaryDescriptorMatcher.knnMatch(	queryDescriptors, trainDescriptors, k[, mask[, compactResult]]	) ->	matches
	cv.line_descriptor.BinaryDescriptorMatcher.knnMatchQuery(	queryDescriptors, matches, k[, masks[, compactResult]]	) ->	None

For every input query descriptor, retrieve the best k matching ones from a dataset provided from user or from the one internal to class.

Parameters

queryDescriptors	query descriptors
trainDescriptors	dataset of descriptors furnished by user
matches	vector to host retrieved matches
k	number of the closest descriptors to be returned for every input query
mask	mask to select which input descriptors must be matched to ones in dataset
compactResult	flag to obtain a compact result (if true, a vector that doesn't contain any matches for a given query is not inserted in final result)

knnMatch() [2/2]

void cv::line_descriptor::BinaryDescriptorMatcher::knnMatch	(	const Mat &	queryDescriptors,
		std::vector< std::vector< DMatch > > &	matches,
		int	k,
		const std::vector< Mat > &	masks = std::vector< Mat >(),
		bool	compactResult = false )

Python:
	cv.line_descriptor.BinaryDescriptorMatcher.knnMatch(	queryDescriptors, trainDescriptors, k[, mask[, compactResult]]	) ->	matches
	cv.line_descriptor.BinaryDescriptorMatcher.knnMatchQuery(	queryDescriptors, matches, k[, masks[, compactResult]]	) ->	None

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

queryDescriptors	query descriptors
matches	vector to host retrieved matches
k	number of the closest descriptors to be returned for every input query
masks	vector of masks to select which input descriptors must be matched to ones in dataset (the i-th mask in vector indicates whether each input query can be matched with descriptors in dataset relative to i-th image)
compactResult	flag to obtain a compact result (if true, a vector that doesn't contain any matches for a given query is not inserted in final result)

match() [1/2]

void cv::line_descriptor::BinaryDescriptorMatcher::match	(	const Mat &	queryDescriptors,
		const Mat &	trainDescriptors,
		std::vector< DMatch > &	matches,
		const Mat &	mask = Mat() ) const

Python:
	cv.line_descriptor.BinaryDescriptorMatcher.match(	queryDescriptors, trainDescriptors[, mask]	) ->	matches
	cv.line_descriptor.BinaryDescriptorMatcher.matchQuery(	queryDescriptors[, masks]	) ->	matches

For every input query descriptor, retrieve the best matching one from a dataset provided from user or from the one internal to class.

Parameters

queryDescriptors	query descriptors
trainDescriptors	dataset of descriptors furnished by user
matches	vector to host retrieved matches
mask	mask to select which input descriptors must be matched to one in dataset

match() [2/2]

void cv::line_descriptor::BinaryDescriptorMatcher::match	(	const Mat &	queryDescriptors,
		std::vector< DMatch > &	matches,
		const std::vector< Mat > &	masks = std::vector< Mat >() )

Python:
	cv.line_descriptor.BinaryDescriptorMatcher.match(	queryDescriptors, trainDescriptors[, mask]	) ->	matches
	cv.line_descriptor.BinaryDescriptorMatcher.matchQuery(	queryDescriptors[, masks]	) ->	matches

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

queryDescriptors	query descriptors
matches	vector to host retrieved matches
masks	vector of masks to select which input descriptors must be matched to one in dataset (the i-th mask in vector indicates whether each input query can be matched with descriptors in dataset relative to i-th image)

radiusMatch() [1/2]

void cv::line_descriptor::BinaryDescriptorMatcher::radiusMatch	(	const Mat &	queryDescriptors,
		const Mat &	trainDescriptors,
		std::vector< std::vector< DMatch > > &	matches,
		float	maxDistance,
		const Mat &	mask = Mat(),
		bool	compactResult = false ) const

For every input query descriptor, retrieve, from a dataset provided from user or from the one internal to class, all the descriptors that are not further than maxDist from input query.

Parameters

queryDescriptors	query descriptors
trainDescriptors	dataset of descriptors furnished by user
matches	vector to host retrieved matches
maxDistance	search radius
mask	mask to select which input descriptors must be matched to ones in dataset
compactResult	flag to obtain a compact result (if true, a vector that doesn't contain any matches for a given query is not inserted in final result)

radiusMatch() [2/2]

void cv::line_descriptor::BinaryDescriptorMatcher::radiusMatch	(	const Mat &	queryDescriptors,
		std::vector< std::vector< DMatch > > &	matches,
		float	maxDistance,
		const std::vector< Mat > &	masks = std::vector< Mat >(),
		bool	compactResult = false )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

queryDescriptors	query descriptors
matches	vector to host retrieved matches
maxDistance	search radius
masks	vector of masks to select which input descriptors must be matched to ones in dataset (the i-th mask in vector indicates whether each input query can be matched with descriptors in dataset relative to i-th image)
compactResult	flag to obtain a compact result (if true, a vector that doesn't contain any matches for a given query is not inserted in final result)

train()

void cv::line_descriptor::BinaryDescriptorMatcher::train

(

)

Update dataset by inserting into it all descriptors that were stored locally by add function.

Note

Every time this function is invoked, current dataset is deleted and locally stored descriptors are inserted into dataset. The locally stored copy of just inserted descriptors is then removed.

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

• opencv2/line_descriptor/descriptor.hpp