Package org.opencv.xfeatures2d

Class TEBLID

  • public class TEBLID
extends Feature2D
    Class implementing TEBLID (Triplet-based Efficient Binary Local Image Descriptor), described in CITE: Suarez2021TEBLID. TEBLID stands for Triplet-based Efficient Binary Local Image Descriptor, although originally it was called BAD \cite Suarez2021TEBLID. It is an improvement over BEBLID \cite Suarez2020BEBLID, that uses triplet loss, hard negative mining, and anchor swap to improve the image matching results. It is able to describe keypoints from any detector just by changing the scale_factor parameter. TEBLID is as efficient as ORB, BEBLID or BRISK, but the triplet-based training objective selected more discriminative features that explain the accuracy gain. It is also more compact than BEBLID, when running the [AKAZE example](https://github.com/opencv/opencv/blob/5.x/samples/cpp/tutorial_code/features2D/AKAZE_match.cpp) with 10000 keypoints detected by ORB, BEBLID obtains 561 inliers (75%) with 512 bits, whereas TEBLID obtains 621 (75.2%) with 256 bits. ORB obtains only 493 inliers (63%). If you find this code useful, please add a reference to the following paper: <BLOCKQUOTE> Iago Suárez, José M. Buenaposada, and Luis Baumela. Revisiting Binary Local Image Description for Resource Limited Devices. IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 8317-8324, Oct. 2021. </BLOCKQUOTE> The descriptor was trained in Liberty split of the UBC datasets \cite winder2007learning .

    • Constructor Detail

      • TEBLID

        protected TEBLID​(long addr)

    • Method Detail

      • __fromPtr__

        public static TEBLID __fromPtr__​(long addr)

      • create

        public static TEBLID create​(float scale_factor,
                            int n_bits)
        Creates the TEBLID descriptor.
        Parameters:
        scale_factor - Adjust the sampling window around detected keypoints:
        • <b> 1.00f </b> should be the scale for ORB keypoints
        • <b> 6.75f </b> should be the scale for SIFT detected keypoints
        • <b> 6.25f </b> is default and fits for KAZE, SURF detected keypoints
        • <b> 5.00f </b> should be the scale for AKAZE, MSD, AGAST, FAST, BRISK keypoints
        n_bits - Determine the number of bits in the descriptor. Should be either TEBLID::SIZE_256_BITS or TEBLID::SIZE_512_BITS.
        Returns:
        automatically generated

      • create

        public static TEBLID create​(float scale_factor)
        Creates the TEBLID descriptor.
        Parameters:
        scale_factor - Adjust the sampling window around detected keypoints:
        • <b> 1.00f </b> should be the scale for ORB keypoints
        • <b> 6.75f </b> should be the scale for SIFT detected keypoints
        • <b> 6.25f </b> is default and fits for KAZE, SURF detected keypoints
        • <b> 5.00f </b> should be the scale for AKAZE, MSD, AGAST, FAST, BRISK keypoints
        TEBLID::SIZE_256_BITS or TEBLID::SIZE_512_BITS.
        Returns:
        automatically generated

      • getDefaultName

        public java.lang.String getDefaultName()
        Description copied from class: Algorithm
        Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.
        Overrides:
        getDefaultName in class Feature2D
        Returns:
        automatically generated

      • finalize

        protected void finalize()
                 throws java.lang.Throwable
        Overrides:
        finalize in class Feature2D
        Throws:
        java.lang.Throwable