Module opencv::xfeatures2d
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Extra 2D Features Framework
Experimental 2D Features Algorithms
This section describes experimental algorithms for 2d feature detection.
Non-free 2D Features Algorithms
This section describes two popular algorithms for 2d feature detection, SIFT and SURF, that are known to be patented. You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those. Use them at your own risk.
Experimental 2D Features Matching Algorithm
This section describes the following matching strategies:
- GMS: Grid-based Motion Statistics, Bian2017gms
- LOGOS: Local geometric support for high-outlier spatial verification, Lowry2018LOGOSLG
Modules
Structs
- Elliptic region around an interest point.
- Class used for extracting Speeded Up Robust Features (SURF) from an image. :
- Class implementing TEBLID (Triplet-based Efficient Binary Local Image Descriptor), described in Suarez2021TEBLID.
Enums
- Descriptor number of bits, each bit is a boosting weak-learner. The user can choose between 512 or 256 bits.
- Lp distance function selector.
- Point distributions supported by random point generator.
- Similarity function selector.
- Descriptor number of bits, each bit is a box average difference. The user can choose between 256 or 512 bits.
Constants
- block formula
- block formula
- block formula
- Generate points with normal (gaussian) distribution.
- Generate points in a regular grid.
- Generate numbers uniformly.
Traits
- Class implementing affine adaptation for key points.
- Constant methods for crate::xfeatures2d::AffineFeature2D
- Class implementing BEBLID (Boosted Efficient Binary Local Image Descriptor), described in Suarez2020BEBLID .
- Constant methods for crate::xfeatures2d::BEBLID
- Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in Trzcinski13a and Trzcinski13b.
- Constant methods for crate::xfeatures2d::BoostDesc
- Class for computing BRIEF descriptors described in calon2010 .
- Constant methods for crate::xfeatures2d::BriefDescriptorExtractor
- Class implementing DAISY descriptor, described in Tola10
- Constant methods for crate::xfeatures2d::DAISY
- Mutable methods for crate::xfeatures2d::Elliptic_KeyPoint
- Constant methods for crate::xfeatures2d::Elliptic_KeyPoint
- Class implementing the FREAK (Fast Retina Keypoint) keypoint descriptor, described in AOV12 .
- Constant methods for crate::xfeatures2d::FREAK
- Class implementing the Harris-Laplace feature detector as described in Mikolajczyk2004.
- Constant methods for crate::xfeatures2d::HarrisLaplaceFeatureDetector
- 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
- Constant methods for crate::xfeatures2d::LATCH
- Class implementing the locally uniform comparison image descriptor, described in LUCID
- Constant methods for crate::xfeatures2d::LUCID
- Class implementing the MSD (Maximal Self-Dissimilarity) keypoint detector, described in Tombari14.
- Constant methods for crate::xfeatures2d::MSDDetector
- Class implementing PCT (position-color-texture) signature extraction as described in KrulisLS16. The algorithm is divided to a feature sampler and a clusterizer. Feature sampler produces samples at given set of coordinates. Clusterizer then produces clusters of these samples using k-means algorithm. Resulting set of clusters is the signature of the input image.
- Constant methods for crate::xfeatures2d::PCTSignatures
- Class implementing Signature Quadratic Form Distance (SQFD).
- Constant methods for crate::xfeatures2d::PCTSignaturesSQFD
- Class for extracting Speeded Up Robust Features from an image Bay06 .
- Constant methods for crate::xfeatures2d::SURF
- Mutable methods for crate::xfeatures2d::SURF_CUDA
- Constant methods for crate::xfeatures2d::SURF_CUDA
- The class implements the keypoint detector introduced by Agrawal08, synonym of StarDetector. :
- Constant methods for crate::xfeatures2d::StarDetector
- Class implementing the Tree Based Morse Regions (TBMR) as described in Najman2014 extended with scaled extraction ability.
- Constant methods for crate::xfeatures2d::TBMR
- Mutable methods for crate::xfeatures2d::TEBLID
- Constant methods for crate::xfeatures2d::TEBLID
- Class implementing VGG (Oxford Visual Geometry Group) descriptor trained end to end using “Descriptor Learning Using Convex Optimisation” (DLCO) aparatus described in Simonyan14.
- Constant methods for crate::xfeatures2d::VGG
Functions
- Estimates cornerness for prespecified KeyPoints using the FAST algorithm
- GMS (Grid-based Motion Statistics) feature matching strategy described in Bian2017gms .
- LOGOS (Local geometric support for high-outlier spatial verification) feature matching strategy described in Lowry2018LOGOSLG .