Namespace OpenCvSharp.XFeatures2D
Classes
- AKAZE
Class implementing the AKAZE keypoint detector and descriptor extractor, described in @cite ANB13
- AffineFeature2D
Class implementing affine adaptation for keypoints. A Feature2D detector and a Feature2D descriptor extractor are wrapped to augment the detected points with their affine invariant elliptic region, and to compute the feature descriptors on the regions after warping them into circles.
- AgastFeatureDetector
Detects corners using the AGAST algorithm
- BEBLID
Class implementing BEBLID (Boosted Efficient Binary Local Image Descriptor), a binary descriptor learned with boosting.
- BOWImgDescriptorExtractor
Brute-force descriptor matcher. For each descriptor in the first set, this matcher finds the closest descriptor in the second set by trying each one.
- BOWKMeansTrainer
Brute-force descriptor matcher. For each descriptor in the first set, this matcher finds the closest descriptor in the second set by trying each one.
- BOWTrainer
Brute-force descriptor matcher. For each descriptor in the first set, this matcher finds the closest descriptor in the second set by trying each one.
- BRISK
BRISK implementation
- BoostDesc
Class implementing BoostDesc (Learning Image Descriptors with Boosting).
- BriefDescriptorExtractor
BRIEF Descriptor
- DAISY
Class implementing the DAISY descriptor.
- FREAK
FREAK implementation
- HarrisLaplaceFeatureDetector
Class implementing the Harris-Laplace feature detector.
- KAZE
Class implementing the KAZE keypoint detector and descriptor extractor
- LATCH
LATCH Descriptor.
latch Class for computing the LATCH descriptor. If you find this code useful, please add a reference to the following paper in your work: Gil Levi and Tal Hassner, "LATCH: Learned Arrangements of Three Patch Codes", arXiv preprint arXiv:1501.03719, 15 Jan. 2015.
Note: a complete example can be found under /samples/cpp/tutorial_code/xfeatures2D/latch_match.cpp
- LUCID
Class implementing the locally uniform comparison image descriptor, described in @cite LUCID.
An image descriptor that can be computed very fast, while being about as robust as, for example, SURF or BRIEF. @note It requires a color image as input.
- MSDDetector
Class implementing the MSD (Maximal Self-Dissimilarity) keypoint detector.
- PCTSignatures
Class implementing PCT (position-color-texture) signature extraction, as described in KrulisLS16. The algorithm is divided into a feature sampler and a clusterizer. The feature sampler produces samples at a given set of coordinates, and the clusterizer produces clusters of these samples using the k-means algorithm; the resulting set of clusters is the signature of the input image.
- PCTSignaturesSQFD
Class implementing Signature Quadratic Form Distance (SQFD).
- SURF
Class for extracting Speeded Up Robust Features from an image.
- StarDetector
The "Star" Detector
- TBMR
Class implementing the Tree Based Morse Regions (TBMR) detector, extended with scaled extraction ability. Extends AffineFeature2D (mirroring cv::xfeatures2d::TBMR : AffineFeature2D), so DetectElliptic/DetectAndComputeElliptic are available in addition to the plain Feature2D API.
- TEBLID
Class implementing TEBLID (Triplet-based Efficient Binary Local Image Descriptor), an improvement over BEBLID that uses triplet loss, hard negative mining, and anchor swap.
- VGG
Class implementing the VGG (Oxford Visual Geometry Group) descriptor, trained end-to-end using "Descriptor Learning Using Convex Optimisation" (DLCO).
Structs
- EllipticKeyPoint
Elliptic region around an interest point, as produced by AffineFeature2D. Mirrors cv::xfeatures2d::Elliptic_KeyPoint.
Enums
- AgastFeatureDetector.DetectorType
AGAST type one of the four neighborhoods as defined in the paper