Static Public Member Functions
static void	matchGMS (Size size1, Size size2, MatOfKeyPoint keypoints1, MatOfKeyPoint keypoints2, MatOfDMatch matches1to2, MatOfDMatch matchesGMS, bool withRotation, bool withScale, double thresholdFactor)
	GMS (Grid-based Motion Statistics) feature matching strategy described in [Bian2017gms] . More...

static void	matchGMS (Size size1, Size size2, MatOfKeyPoint keypoints1, MatOfKeyPoint keypoints2, MatOfDMatch matches1to2, MatOfDMatch matchesGMS, bool withRotation, bool withScale)
	GMS (Grid-based Motion Statistics) feature matching strategy described in [Bian2017gms] . More...

static void	matchGMS (Size size1, Size size2, MatOfKeyPoint keypoints1, MatOfKeyPoint keypoints2, MatOfDMatch matches1to2, MatOfDMatch matchesGMS, bool withRotation)
	GMS (Grid-based Motion Statistics) feature matching strategy described in [Bian2017gms] . More...

static void	matchGMS (Size size1, Size size2, MatOfKeyPoint keypoints1, MatOfKeyPoint keypoints2, MatOfDMatch matches1to2, MatOfDMatch matchesGMS)
	GMS (Grid-based Motion Statistics) feature matching strategy described in [Bian2017gms] . More...

static void	matchLOGOS (MatOfKeyPoint keypoints1, MatOfKeyPoint keypoints2, MatOfInt nn1, MatOfInt nn2, MatOfDMatch matches1to2)
	LOGOS (Local geometric support for high-outlier spatial verification) feature matching strategy described in [Lowry2018LOGOSLG] . More...

Member Function Documentation

◆ matchGMS() [1/4]

static void OpenCVForUnity.Xfeatures2dModule.Xfeatures2d.matchGMS	(	Size	size1,
		Size	size2,
		MatOfKeyPoint	keypoints1,
		MatOfKeyPoint	keypoints2,
		MatOfDMatch	matches1to2,
		MatOfDMatch	matchesGMS,
		bool	withRotation,
		bool	withScale,
		double	thresholdFactor
	)

static

GMS (Grid-based Motion Statistics) feature matching strategy described in [Bian2017gms] .

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.

static void OpenCVForUnity.Xfeatures2dModule.Xfeatures2d.matchGMS	(	Size	size1,
		Size	size2,
		MatOfKeyPoint	keypoints1,
		MatOfKeyPoint	keypoints2,
		MatOfDMatch	matches1to2,
		MatOfDMatch	matchesGMS,
		bool	withRotation,
		bool	withScale
	)

static

GMS (Grid-based Motion Statistics) feature matching strategy described in [Bian2017gms] .

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.

static void OpenCVForUnity.Xfeatures2dModule.Xfeatures2d.matchGMS	(	Size	size1,
		Size	size2,
		MatOfKeyPoint	keypoints1,
		MatOfKeyPoint	keypoints2,
		MatOfDMatch	matches1to2,
		MatOfDMatch	matchesGMS,
		bool	withRotation
	)

static

GMS (Grid-based Motion Statistics) feature matching strategy described in [Bian2017gms] .

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.

static void OpenCVForUnity.Xfeatures2dModule.Xfeatures2d.matchGMS	(	Size	size1,
		Size	size2,
		MatOfKeyPoint	keypoints1,
		MatOfKeyPoint	keypoints2,
		MatOfDMatch	matches1to2,
		MatOfDMatch	matchesGMS
	)

static

GMS (Grid-based Motion Statistics) feature matching strategy described in [Bian2017gms] .

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.

static void OpenCVForUnity.Xfeatures2dModule.Xfeatures2d.matchLOGOS	(	MatOfKeyPoint	keypoints1,
		MatOfKeyPoint	keypoints2,
		MatOfInt	nn1,
		MatOfInt	nn2,
		MatOfDMatch	matches1to2
	)

static

LOGOS (Local geometric support for high-outlier spatial verification) feature matching strategy described in [Lowry2018LOGOSLG] .

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).

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

OpenCVForUnity/Assets/OpenCVForUnity/org/opencv_contrib/xfeatures2d/Xfeatures2d.cs