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Classes | |
| class | AffineFeature2D |
| Class implementing affine adaptation for key points. More... | |
| class | BEBLID |
| Class implementing BEBLID (Boosted Efficient Binary Local Image Descriptor), described in [Suarez2020BEBLID] . More... | |
| class | BoostDesc |
| Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in [Trzcinski13a] and [Trzcinski13b]. More... | |
| class | BriefDescriptorExtractor |
| Class for computing BRIEF descriptors described in [calon2010] . More... | |
| class | DAISY |
| Class implementing DAISY descriptor, described in [Tola10]. More... | |
| class | Elliptic_KeyPoint |
| Elliptic region around an interest point. More... | |
| class | FREAK |
| Class implementing the FREAK (Fast Retina Keypoint) keypoint descriptor, described in [AOV12] . More... | |
| class | HarrisLaplaceFeatureDetector |
| Class implementing the Harris-Laplace feature detector as described in [Mikolajczyk2004]. More... | |
| class | LATCH |
| class | LUCID |
| Class implementing the locally uniform comparison image descriptor, described in [LUCID]. More... | |
| class | MSDDetector |
| Class implementing the MSD (Maximal Self-Dissimilarity) keypoint detector, described in [Tombari14]. More... | |
| class | 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. More... | |
| class | PCTSignaturesSQFD |
| Class implementing Signature Quadratic Form Distance (SQFD). More... | |
| class | StarDetector |
| The class implements the keypoint detector introduced by [Agrawal08], synonym of StarDetector. : More... | |
| class | SURF |
| Class for extracting Speeded Up Robust Features from an image [Bay06] . More... | |
| class | TBMR |
| Class implementing the Tree Based Morse Regions (TBMR) as described in [Najman2014] extended with scaled extraction ability. More... | |
| class | TEBLID |
| Class implementing TEBLID (Triplet-based Efficient Binary Local Image Descriptor), described in [Suarez2021TEBLID]. More... | |
| class | VGG |
| Class implementing VGG (Oxford Visual Geometry Group) descriptor trained end to end using "Descriptor Learning Using Convex Optimisation" (DLCO) aparatus described in [Simonyan14]. More... | |
Typedefs | |
| typedef SURF | SurfDescriptorExtractor |
| typedef SURF | SurfFeatureDetector |
Functions | |
| void | 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. | |
| void | 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 [Bian2017gms] . | |
| void | 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 [Lowry2018LOGOSLG] . | |
