Class Xfeatures2d


  • public class Xfeatures2d
    extends java.lang.Object
    • 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).