## Class Xfeatures2d

• java.lang.Object
• org.opencv.xfeatures2d.Xfeatures2d

• public class Xfeatures2d
extends java.lang.Object
• ### Constructor Summary

Constructors
Constructor Description
Xfeatures2d()
• ### Method Summary

All Methods
Modifier and Type Method Description
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 CITE: Bian2017gms .
static void matchGMS​(Size size1, Size size2, MatOfKeyPoint keypoints1, MatOfKeyPoint keypoints2, MatOfDMatch matches1to2, MatOfDMatch matchesGMS, boolean withRotation)
GMS (Grid-based Motion Statistics) feature matching strategy described in CITE: Bian2017gms .
static void matchGMS​(Size size1, Size size2, MatOfKeyPoint keypoints1, MatOfKeyPoint keypoints2, MatOfDMatch matches1to2, MatOfDMatch matchesGMS, boolean withRotation, boolean withScale)
GMS (Grid-based Motion Statistics) feature matching strategy described in CITE: Bian2017gms .
static void matchGMS​(Size size1, Size size2, MatOfKeyPoint keypoints1, MatOfKeyPoint keypoints2, MatOfDMatch matches1to2, MatOfDMatch matchesGMS, boolean withRotation, boolean withScale, double thresholdFactor)
GMS (Grid-based Motion Statistics) feature matching strategy described in CITE: Bian2017gms .
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 CITE: Lowry2018LOGOSLG .
• ### Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
• ### Constructor Detail

• #### Xfeatures2d

public Xfeatures2d()
• ### Method Detail

• #### matchGMS

public static void matchGMS​(Size size1,
Size size2,
MatOfKeyPoint keypoints1,
MatOfKeyPoint keypoints2,
MatOfDMatch matches1to2,
MatOfDMatch matchesGMS,
boolean withRotation,
boolean withScale,
double thresholdFactor)
GMS (Grid-based Motion Statistics) feature matching strategy described in CITE: 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.
• #### matchGMS

public static void matchGMS​(Size size1,
Size size2,
MatOfKeyPoint keypoints1,
MatOfKeyPoint keypoints2,
MatOfDMatch matches1to2,
MatOfDMatch matchesGMS,
boolean withRotation,
boolean withScale)
GMS (Grid-based Motion Statistics) feature matching strategy described in CITE: 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. 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.
• #### matchGMS

public static void matchGMS​(Size size1,
Size size2,
MatOfKeyPoint keypoints1,
MatOfKeyPoint keypoints2,
MatOfDMatch matches1to2,
MatOfDMatch matchesGMS,
boolean withRotation)
GMS (Grid-based Motion Statistics) feature matching strategy described in CITE: 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. 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.
• #### matchGMS

public 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 CITE: 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. 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

public 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 CITE: 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).