Package org.opencv.xfeatures2d
Class Xfeatures2d
- java.lang.Object
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- org.opencv.xfeatures2d.Xfeatures2d
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public class Xfeatures2d extends java.lang.Object
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Constructor Summary
Constructors Constructor Description Xfeatures2d()
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Method Summary
All Methods Static Methods Concrete 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 .
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Method Detail
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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.
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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.
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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.
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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.
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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).
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