Uses of Class
org.opencv.features2d.Feature2D

Packages that use Feature2D

Package	Description
org.opencv.features2d
org.opencv.xfeatures2d

Uses of Feature2D in org.opencv.features2d

Subclasses of Feature2D in org.opencv.features2d

Modifier and Type	Class and Description
class	AgastFeatureDetector Wrapping class for feature detection using the AGAST method.
class	AKAZE Class implementing the AKAZE keypoint detector and descriptor extractor, described in CITE: ANB13.
class	BRISK Class implementing the BRISK keypoint detector and descriptor extractor, described in CITE: LCS11 .
class	FastFeatureDetector Wrapping class for feature detection using the FAST method.
class	GFTTDetector Wrapping class for feature detection using the goodFeaturesToTrack function.
class	KAZE Class implementing the KAZE keypoint detector and descriptor extractor, described in CITE: ABD12 .
class	MSER Maximally stable extremal region extractor The class encapsulates all the parameters of the %MSER extraction algorithm (see [wiki article](http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions)).
class	ORB Class implementing the ORB (*oriented BRIEF*) keypoint detector and descriptor extractor described in CITE: RRKB11 .
class	SimpleBlobDetector Class for extracting blobs from an image.

Methods in org.opencv.features2d that return Feature2D

Modifier and Type	Method and Description
static Feature2D	Feature2D.__fromPtr__(long addr)

Uses of Feature2D in org.opencv.xfeatures2d

Subclasses of Feature2D in org.opencv.xfeatures2d

Modifier and Type	Class and Description
class	BoostDesc Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in CITE: Trzcinski13a and CITE: Trzcinski13b.
class	BriefDescriptorExtractor Class for computing BRIEF descriptors described in CITE: calon2010 .
class	DAISY Class implementing DAISY descriptor, described in CITE: Tola10 radius radius of the descriptor at the initial scale q_radius amount of radial range division quantity q_theta amount of angular range division quantity q_hist amount of gradient orientations range division quantity norm choose descriptors normalization type, where DAISY::NRM_NONE will not do any normalization (default), DAISY::NRM_PARTIAL mean that histograms are normalized independently for L2 norm equal to 1.0, DAISY::NRM_FULL mean that descriptors are normalized for L2 norm equal to 1.0, DAISY::NRM_SIFT mean that descriptors are normalized for L2 norm equal to 1.0 but no individual one is bigger than 0.154 as in SIFT H optional 3x3 homography matrix used to warp the grid of daisy but sampling keypoints remains unwarped on image interpolation switch to disable interpolation for speed improvement at minor quality loss use_orientation sample patterns using keypoints orientation, disabled by default.
class	FREAK Class implementing the FREAK (*Fast Retina Keypoint*) keypoint descriptor, described in CITE: AOV12 .
class	HarrisLaplaceFeatureDetector Class implementing the Harris-Laplace feature detector as described in CITE: Mikolajczyk2004.
class	LATCH latch Class for computing the LATCH descriptor.
class	LUCID Class implementing the locally uniform comparison image descriptor, described in CITE: LUCID An image descriptor that can be computed very fast, while being about as robust as, for example, SURF or BRIEF.
class	MSDDetector Class implementing the MSD (*Maximal Self-Dissimilarity*) keypoint detector, described in CITE: Tombari14.
class	SIFT Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) algorithm by D.
class	StarDetector The class implements the keypoint detector introduced by CITE: Agrawal08, synonym of StarDetector.
class	SURF Class for extracting Speeded Up Robust Features from an image CITE: Bay06 .
class	VGG Class implementing VGG (Oxford Visual Geometry Group) descriptor trained end to end using "Descriptor Learning Using Convex Optimisation" (DLCO) aparatus described in CITE: Simonyan14.