|
| class | AffineFeature2D |
| | Class implementing affine adaptation for key points. More...
|
| |
| class | BEBLID |
| | Class implementing BEBLID (Boosted Efficient Binary Local Image Descriptor), described in [226] . More...
|
| |
| class | BoostDesc |
| | Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in [230] and [231]. More...
|
| |
| class | BriefDescriptorExtractor |
| | Class for computing BRIEF descriptors described in [35] . More...
|
| |
| class | DAISY |
| | Class implementing DAISY descriptor, described in [239]. More...
|
| |
| class | Elliptic_KeyPoint |
| | Elliptic region around an interest point. More...
|
| |
| class | FREAK |
| | Class implementing the FREAK (Fast Retina Keypoint) keypoint descriptor, described in [5] . More...
|
| |
| class | HarrisLaplaceFeatureDetector |
| | Class implementing the Harris-Laplace feature detector as described in [166]. More...
|
| |
| class | LATCH |
| |
| class | LUCID |
| | Class implementing the locally uniform comparison image descriptor, described in [287]. More...
|
| |
| class | MSDDetector |
| | Class implementing the MSD (Maximal Self-Dissimilarity) keypoint detector, described in [240]. More...
|
| |
| class | PCTSignatures |
| | Class implementing PCT (position-color-texture) signature extraction as described in [127]. 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 [2], synonym of StarDetector. : More...
|
| |
| class | SURF |
| | Class for extracting Speeded Up Robust Features from an image [13] . More...
|
| |
| class | TBMR |
| | Class implementing the Tree Based Morse Regions (TBMR) as described in [273] extended with scaled extraction ability. 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 [217]. More...
|
| |
|
| 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. More...
|
| |
| 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 [18] . More...
|
| |
| 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 [149] . More...
|
| |
1.8.13