OpenCV
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 [234] . More... | |
class | cv::xfeatures2d::BoostDesc |
Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in [238] and [239]. More... | |
class | cv::xfeatures2d::BriefDescriptorExtractor |
Class for computing BRIEF descriptors described in [41] . More... | |
class | cv::xfeatures2d::DAISY |
Class implementing DAISY descriptor, described in [247]. 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 [173]. More... | |
class | cv::xfeatures2d::LATCH |
class | cv::xfeatures2d::LUCID |
Class implementing the locally uniform comparison image descriptor, described in [297]. More... | |
class | cv::xfeatures2d::MSDDetector |
Class implementing the MSD (Maximal Self-Dissimilarity) keypoint detector, described in [248]. More... | |
class | cv::xfeatures2d::PCTSignatures |
Class implementing PCT (position-color-texture) signature extraction as described in [133]. 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 [283] extended with scaled extraction ability. 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 [225]. More... | |
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... | |
This section describes experimental algorithms for 2d feature detection.
void cv::xfeatures2d::FASTForPointSet | ( | InputArray | image, |
std::vector< KeyPoint > & | keypoints, | ||
int | threshold, | ||
bool | nonmaxSuppression = true , |
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cv::FastFeatureDetector::DetectorType | type = FastFeatureDetector::TYPE_9_16 |
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#include <opencv2/xfeatures2d.hpp>
Estimates cornerness for prespecified KeyPoints using the FAST algorithm.
image | grayscale image where keypoints (corners) are detected. |
keypoints | keypoints which should be tested to fit the FAST criteria. Keypoints not being detected as corners are removed. |
threshold | threshold on difference between intensity of the central pixel and pixels of a circle around this pixel. |
nonmaxSuppression | if true, non-maximum suppression is applied to detected corners (keypoints). |
type | one of the three neighborhoods as defined in the paper: FastFeatureDetector::TYPE_9_16, FastFeatureDetector::TYPE_7_12, FastFeatureDetector::TYPE_5_8 |
Detects corners using the FAST algorithm by [207] .