OpenCV
4.10.0-dev
Open Source Computer Vision
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Classes | |
class | cv::xfeatures2d::AffineFeature2D |
Class implementing affine adaptation for key points. More... | |
class | cv::xfeatures2d::BEBLID |
Class implementing BEBLID (Boosted Efficient Binary Local Image Descriptor), described in [256] . More... | |
class | cv::xfeatures2d::BoostDesc |
Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in [261] and [262]. More... | |
class | cv::xfeatures2d::BriefDescriptorExtractor |
Class for computing BRIEF descriptors described in [47] . More... | |
class | cv::xfeatures2d::DAISY |
Class implementing DAISY descriptor, described in [270]. More... | |
class | cv::xfeatures2d::Elliptic_KeyPoint |
Elliptic region around an interest point. More... | |
class | cv::xfeatures2d::FREAK |
Class implementing the FREAK (Fast Retina Keypoint) keypoint descriptor, described in [8] . More... | |
class | cv::xfeatures2d::HarrisLaplaceFeatureDetector |
Class implementing the Harris-Laplace feature detector as described in [193]. More... | |
class | cv::xfeatures2d::LATCH |
class | cv::xfeatures2d::LUCID |
Class implementing the locally uniform comparison image descriptor, described in [321]. More... | |
class | cv::xfeatures2d::MSDDetector |
Class implementing the MSD (Maximal Self-Dissimilarity) keypoint detector, described in [271]. More... | |
class | cv::xfeatures2d::PCTSignatures |
Class implementing PCT (position-color-texture) signature extraction as described in [152]. 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. More... | |
class | cv::xfeatures2d::PCTSignaturesSQFD |
Class implementing Signature Quadratic Form Distance (SQFD). More... | |
class | cv::xfeatures2d::StarDetector |
The class implements the keypoint detector introduced by [2], synonym of StarDetector. : More... | |
class | cv::xfeatures2d::TBMR |
Class implementing the Tree Based Morse Regions (TBMR) as described in [307] extended with scaled extraction ability. More... | |
class | cv::xfeatures2d::TEBLID |
Class implementing TEBLID (Triplet-based Efficient Binary Local Image Descriptor), described in [257]. More... | |
class | cv::xfeatures2d::VGG |
Class implementing VGG (Oxford Visual Geometry Group) descriptor trained end to end using "Descriptor Learning Using Convex Optimisation" (DLCO) aparatus described in [246]. More... | |
Namespaces | |
cv | |
cv::xfeatures2d | |
Functions | |
void | cv::xfeatures2d::FASTForPointSet (InputArray image, std::vector< KeyPoint > &keypoints, int threshold, bool nonmaxSuppression=true, cv::FastFeatureDetector::DetectorType type=FastFeatureDetector::TYPE_9_16) |
Estimates cornerness for prespecified KeyPoints using the FAST algorithm. More... | |
void | cv::xfeatures2d::matchGMS (const Size &size1, const Size &size2, const std::vector< KeyPoint > &keypoints1, const std::vector< KeyPoint > &keypoints2, const std::vector< DMatch > &matches1to2, std::vector< DMatch > &matchesGMS, const bool withRotation=false, const bool withScale=false, const double thresholdFactor=6.0) |
GMS (Grid-based Motion Statistics) feature matching strategy described in [26] . More... | |
void | cv::xfeatures2d::matchLOGOS (const std::vector< KeyPoint > &keypoints1, const std::vector< KeyPoint > &keypoints2, const std::vector< int > &nn1, const std::vector< int > &nn2, std::vector< DMatch > &matches1to2) |
LOGOS (Local geometric support for high-outlier spatial verification) feature matching strategy described in [175] . More... | |