Module opencv::xfeatures2d

source ·
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

  • AffineFeature2D
    Class implementing affine adaptation for key points.
  • BEBLID
    Class implementing BEBLID (Boosted Efficient Binary Local Image Descriptor), described in Suarez2020BEBLID .
  • BoostDesc
    Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in Trzcinski13a and Trzcinski13b.
  • BriefDescriptorExtractor
    Class for computing BRIEF descriptors described in calon2010 .
  • DAISY
    Class implementing DAISY descriptor, described in Tola10
  • Elliptic_KeyPoint
    Elliptic region around an interest point.
  • FREAK
    Class implementing the FREAK (Fast Retina Keypoint) keypoint descriptor, described in AOV12 .
  • HarrisLaplaceFeatureDetector
    Class implementing the Harris-Laplace feature detector as described in Mikolajczyk2004.
  • LATCH
    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
  • LUCID
    Class implementing the locally uniform comparison image descriptor, described in LUCID
  • MSDDetector
    Class implementing the MSD (Maximal Self-Dissimilarity) keypoint detector, described in Tombari14.
  • PCTSignatures
    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.
  • PCTSignaturesSQFD
    Class implementing Signature Quadratic Form Distance (SQFD).
  • SURF
    Class for extracting Speeded Up Robust Features from an image Bay06 .
  • SURF_CUDA
    Class used for extracting Speeded Up Robust Features (SURF) from an image. :
  • StarDetector
    The class implements the keypoint detector introduced by Agrawal08, synonym of StarDetector. :
  • TBMR
    Class implementing the Tree Based Morse Regions (TBMR) as described in Najman2014 extended with scaled extraction ability.
  • TEBLID
    Class implementing TEBLID (Triplet-based Efficient Binary Local Image Descriptor), described in Suarez2021TEBLID.
  • VGG
    Class implementing VGG (Oxford Visual Geometry Group) descriptor trained end to end using “Descriptor Learning Using Convex Optimisation” (DLCO) aparatus described in Simonyan14.

Enums

Constants

Traits

Functions

Type Definitions