Feature Detection and Description#

Detailed Description#

Classes#

Name	Description
`class` `cv::cuda::CornernessCriteria`	Base class for Cornerness Criteria computation. : View details
`class` `cv::cuda::CornersDetector`	Base class for Corners Detector. : View details
`class` `cv::cuda::DescriptorMatcher`	Abstract base class for matching keypoint descriptors. View details
`class` `cv::cuda::FastFeatureDetector`	Wrapping class for feature detection using the FAST method. View details
`class` `cv::cuda::Feature2DAsync`	Abstract base class for CUDA asynchronous 2D image feature detectors and descriptor extractors. View details
`class` `cv::cuda::ORB`	Class implementing the ORB (oriented BRIEF) keypoint detector and descriptor extractor. View details

Function Documentation#

createGoodFeaturesToTrackDetector()#

Ptr< CornersDetector > cv::cuda::createGoodFeaturesToTrackDetector(
int srcType,
int maxCorners = 1000,
double qualityLevel = 0.01,
double minDistance = 0.0,
int blockSize = 3,
bool useHarrisDetector = false,
double harrisK = 0.04 )

#include <opencv2/cudafeatures2d.hpp>

Creates implementation for cuda::CornersDetector .

Parameters

srcType — Input source type. Only CV_8UC1 and CV_32FC1 are supported for now.
maxCorners — Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned.
qualityLevel — Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see cornerMinEigenVal ) or the Harris function response (see cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
minDistance — Minimum possible Euclidean distance between the returned corners.
blockSize — Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs .
useHarrisDetector — Parameter indicating whether to use a Harris detector (see cornerHarris) or cornerMinEigenVal.
harrisK — Free parameter of the Harris detector.

createHarrisCorner()#

Ptr< CornernessCriteria > cv::cuda::createHarrisCorner(
int srcType,
int blockSize,
int ksize,
double k,
int borderType = BORDER_REFLECT101 )

#include <opencv2/cudafeatures2d.hpp>

Creates implementation for Harris cornerness criteria.

createMinEigenValCorner()#

Ptr< CornernessCriteria > cv::cuda::createMinEigenValCorner(
int srcType,
int blockSize,
int ksize,
int borderType = BORDER_REFLECT101 )

#include <opencv2/cudafeatures2d.hpp>

Creates implementation for the minimum eigen value of a 2x2 derivative covariation matrix (the cornerness criteria).