Experimental 2D Features Matching Algorithm

Functions
void	cv::xfeatures2d::matchGMS (const Size &size1, const Size &size2, const std::vector< KeyPoint > &keypoints1, const std::vector< KeyPoint > &keypoints2, const std::vector< DMatch > &matches1to2, std::vector< DMatch > &matchesGMS, const bool withRotation=false, const bool withScale=false, const double thresholdFactor=6.0)
	GMS (Grid-based Motion Statistics) feature matching strategy described in [23] . More...
void	cv::xfeatures2d::matchLOGOS (const std::vector< KeyPoint > &keypoints1, const std::vector< KeyPoint > &keypoints2, const std::vector< int > &nn1, const std::vector< int > &nn2, std::vector< DMatch > &matches1to2)
	LOGOS (Local geometric support for high-outlier spatial verification) feature matching strategy described in [162] . More...

Detailed Description

This section describes the following matching strategies:

• GMS: Grid-based Motion Statistics, [23]
• LOGOS: Local geometric support for high-outlier spatial verification, [162]

Function Documentation

matchGMS()

void cv::xfeatures2d::matchGMS	(	const Size &	size1,
		const Size &	size2,
		const std::vector< KeyPoint > &	keypoints1,
		const std::vector< KeyPoint > &	keypoints2,
		const std::vector< DMatch > &	matches1to2,
		std::vector< DMatch > &	matchesGMS,
		const bool	withRotation = false,
		const bool	withScale = false,
		const double	thresholdFactor = 6.0
	)

Python:
	cv.xfeatures2d.matchGMS(	size1, size2, keypoints1, keypoints2, matches1to2[, withRotation[, withScale[, thresholdFactor]]]	) ->	matchesGMS

#include <opencv2/xfeatures2d.hpp>

GMS (Grid-based Motion Statistics) feature matching strategy described in [23] .

Parameters

size1	Input size of image1.
size2	Input size of image2.
keypoints1	Input keypoints of image1.
keypoints2	Input keypoints of image2.
matches1to2	Input 1-nearest neighbor matches.
matchesGMS	Matches returned by the GMS matching strategy.
withRotation	Take rotation transformation into account.
withScale	Take scale transformation into account.
thresholdFactor	The higher, the less matches.

Note

Since GMS works well when the number of features is large, we recommend to use the ORB feature and set FastThreshold to 0 to get as many as possible features quickly. If matching results are not satisfying, please add more features. (We use 10000 for images with 640 X 480). If your images have big rotation and scale changes, please set withRotation or withScale to true.

matchLOGOS()

void cv::xfeatures2d::matchLOGOS	(	const std::vector< KeyPoint > &	keypoints1,
		const std::vector< KeyPoint > &	keypoints2,
		const std::vector< int > &	nn1,
		const std::vector< int > &	nn2,
		std::vector< DMatch > &	matches1to2
	)

Python:
	cv.xfeatures2d.matchLOGOS(	keypoints1, keypoints2, nn1, nn2, matches1to2	) ->	None

#include <opencv2/xfeatures2d.hpp>

LOGOS (Local geometric support for high-outlier spatial verification) feature matching strategy described in [162] .

Parameters

keypoints1	Input keypoints of image1.
keypoints2	Input keypoints of image2.
nn1	Index to the closest BoW centroid for each descriptors of image1.
nn2	Index to the closest BoW centroid for each descriptors of image2.
matches1to2	Matches returned by the LOGOS matching strategy.

Note

This matching strategy is suitable for features matching against large scale database. First step consists in constructing the bag-of-words (BoW) from a representative image database. Image descriptors are then represented by their closest codevector (nearest BoW centroid).