Feature Detection

Detailed Description

Classes
class	cv::cuda::CornernessCriteria
	Base class for Cornerness Criteria computation. : More...
class	cv::cuda::CornersDetector
	Base class for Corners Detector. : More...

Functions
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)
	Creates implementation for cuda::CornersDetector .
Ptr< CornernessCriteria >	cv::cuda::createHarrisCorner (int srcType, int blockSize, int ksize, double k, int borderType=BORDER_REFLECT101)
	Creates implementation for Harris cornerness criteria.
Ptr< CornernessCriteria >	cv::cuda::createMinEigenValCorner (int srcType, int blockSize, int ksize, int borderType=BORDER_REFLECT101)
	Creates implementation for the minimum eigen value of a 2x2 derivative covariation matrix (the cornerness criteria).

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/cudaimgproc.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/cudaimgproc.hpp>

Creates implementation for Harris cornerness criteria.

Parameters

srcType	Input source type. Only CV_8UC1 and CV_32FC1 are supported for now.
blockSize	Neighborhood size.
ksize	Aperture parameter for the Sobel operator.
k	Harris detector free parameter.
borderType	Pixel extrapolation method. Only BORDER_REFLECT101 and BORDER_REPLICATE are supported for now.

See also

cornerHarris

createMinEigenValCorner()

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

#include <opencv2/cudaimgproc.hpp>

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

Parameters

srcType	Input source type. Only CV_8UC1 and CV_32FC1 are supported for now.
blockSize	Neighborhood size.
ksize	Aperture parameter for the Sobel operator.
borderType	Pixel extrapolation method. Only BORDER_REFLECT101 and BORDER_REPLICATE are supported for now.

See also

cornerMinEigenVal