Namespace Emgu.CV.XFeatures2D

BEBLID: Class implementing BEBLID (Boosted Efficient Binary Local Image Descriptor)

BoostDesc: Class implementing BoostDesc (Learning Image Descriptors with Boosting).

Freak: The FREAK (Fast Retina Keypoint) keypoint descriptor: Alahi, R. Ortiz, and P. Vandergheynst. FREAK: Fast Retina Keypoint. In IEEE Conference on Computer Vision and Pattern Recognition, 2012. CVPR 2012 Open Source Award Winner. The algorithm propose a novel keypoint descriptor inspired by the human visual system and more precisely the retina, coined Fast Retina Key- point (FREAK). A cascade of binary strings is computed by efficiently comparing image intensities over a retinal sampling pattern. FREAKs are in general faster to compute with lower memory load and also more robust than SIFT, SURF or BRISK. They are competitive alternatives to existing keypoints in particular for embedded applications.

HarrisLaplaceFeatureDetector: Class implementing the Harris-Laplace feature detector

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 LATCH is a binary descriptor based on learned comparisons of triplets of image patches.

LUCID: The locally uniform comparison image descriptor: An image descriptor that can be computed very fast, while being about as robust as, for example, SURF or BRIEF.

MSDDetector: Class implementing the MSD (Maximal Self-Dissimilarity) keypoint detector, described in "Federico Tombari and Luigi Di Stefano. Interest points via maximal self-dissimilarities. In Asian Conference on Computer Vision - ACCV 2014, 2014".

PCTSignatures: Class implementing PCT (position-color-texture) signature extraction as described in: Martin Krulis, Jakub Lokoc, and Tomas Skopal. Efficient extraction of clustering-based feature signatures using GPU architectures. Multimedia Tools Appl., 75(13):8071–8103, 2016. 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. A signature is an array of SIGNATURE_DIMENSION-dimensional points.Used dimensions are: weight, x, y position; lab color, contrast, entropy.

PCTSignaturesSQFD: Class implementing Signature Quadratic Form Distance (SQFD).

VGG: Class implementing VGG (Oxford Visual Geometry Group) descriptor trained end to end using "Descriptor Learning Using Convex Optimisation" (DLCO) aparatus

XFeatures2DInvoke: This class wraps the functional calls to the OpenCV XFeatures2D modules

PCTSignatures.PointDistributionType: Point distributions supported by random point generator.