Module opencv::xfeatures2d [−][src]
Expand description
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
Class implementing BEBLID (Boosted Efficient Binary Local Image Descriptor), described in Suarez2020BEBLID .
Class for computing BRIEF descriptors described in calon2010 .
Elliptic region around an interest point.
Class implementing the FREAK (Fast Retina Keypoint) keypoint descriptor, described in AOV12 .
Class implementing the Harris-Laplace feature detector as described in Mikolajczyk2004.
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
Class implementing the locally uniform comparison image descriptor, described in LUCID
Class implementing the MSD (Maximal Self-Dissimilarity) keypoint detector, described in Tombari14.
Class used for extracting Speeded Up Robust Features (SURF) from an image. :
The class implements the keypoint detector introduced by Agrawal08, synonym of StarDetector. :
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.
Constants
Traits
Class implementing affine adaptation for key points.
Class implementing BEBLID (Boosted Efficient Binary Local Image Descriptor), described in Suarez2020BEBLID .
Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in Trzcinski13a and Trzcinski13b.
Class for computing BRIEF descriptors described in calon2010 .
Class implementing DAISY descriptor, described in Tola10
Elliptic region around an interest point.
Class implementing the FREAK (Fast Retina Keypoint) keypoint descriptor, described in AOV12 .
Class implementing the Harris-Laplace feature detector as described in Mikolajczyk2004.
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
Class implementing the locally uniform comparison image descriptor, described in LUCID
Class implementing the MSD (Maximal Self-Dissimilarity) keypoint detector, described in Tombari14.
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.
Class implementing Signature Quadratic Form Distance (SQFD).
Class for extracting Speeded Up Robust Features from an image Bay06 .
Class used for extracting Speeded Up Robust Features (SURF) from an image. :
The class implements the keypoint detector introduced by Agrawal08, synonym of StarDetector. :
Class implementing the Tree Based Morse Regions (TBMR) as described in Najman2014 extended with scaled extraction ability.
Class implementing VGG (Oxford Visual Geometry Group) descriptor trained end to end using “Descriptor Learning Using Convex Optimisation” (DLCO) aparatus described in Simonyan14.
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 .