Feature Detection and Description

Classes
class	cv::AgastFeatureDetector
	Wrapping class for feature detection using the AGAST method. : More...
class	cv::AKAZE
	Class implementing the AKAZE keypoint detector and descriptor extractor, described in [5] . : More...
class	cv::BRISK
	Class implementing the BRISK keypoint detector and descriptor extractor, described in [92] . More...
class	cv::FastFeatureDetector
	Wrapping class for feature detection using the FAST method. : More...
class	cv::GFTTDetector
	Wrapping class for feature detection using the goodFeaturesToTrack function. : More...
class	cv::KAZE
	Class implementing the KAZE keypoint detector and descriptor extractor, described in [6] . More...
class	cv::MSER
	Maximally stable extremal region extractor. More...
class	cv::ORB
	Class implementing the ORB (oriented BRIEF) keypoint detector and descriptor extractor. More...
class	cv::SimpleBlobDetector
	Class for extracting blobs from an image. : More...

Functions
void	cv::AGAST (InputArray image, std::vector< KeyPoint > &keypoints, int threshold, bool nonmaxSuppression=true)
void	cv::AGAST (InputArray image, std::vector< KeyPoint > &keypoints, int threshold, bool nonmaxSuppression, int type)
	Detects corners using the AGAST algorithm. More...
void	cv::FAST (InputArray image, std::vector< KeyPoint > &keypoints, int threshold, bool nonmaxSuppression=true)
void	cv::FAST (InputArray image, std::vector< KeyPoint > &keypoints, int threshold, bool nonmaxSuppression, int type)
	Detects corners using the FAST algorithm. More...

Detailed Description

Function Documentation

AGAST() [1/2]

void cv::AGAST	(	InputArray	image,
		std::vector< KeyPoint > &	keypoints,
		int	threshold,
		bool	nonmaxSuppression = true
	)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

AGAST() [2/2]

void cv::AGAST	(	InputArray	image,
		std::vector< KeyPoint > &	keypoints,
		int	threshold,
		bool	nonmaxSuppression,
		int	type
	)

Detects corners using the AGAST algorithm.

Parameters

image	grayscale image where keypoints (corners) are detected.
keypoints	keypoints detected on the image.
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 four neighborhoods as defined in the paper: AgastFeatureDetector::AGAST_5_8, AgastFeatureDetector::AGAST_7_12d, AgastFeatureDetector::AGAST_7_12s, AgastFeatureDetector::OAST_9_16

For non-Intel platforms, there is a tree optimised variant of AGAST with same numerical results. The 32-bit binary tree tables were generated automatically from original code using perl script. The perl script and examples of tree generation are placed in features2d/doc folder. Detects corners using the AGAST algorithm by [103] .

FAST() [1/2]

void cv::FAST	(	InputArray	image,
		std::vector< KeyPoint > &	keypoints,
		int	threshold,
		bool	nonmaxSuppression = true
	)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

FAST() [2/2]

void cv::FAST	(	InputArray	image,
		std::vector< KeyPoint > &	keypoints,
		int	threshold,
		bool	nonmaxSuppression,
		int	type
	)

Detects corners using the FAST algorithm.

Parameters

image	grayscale image where keypoints (corners) are detected.
keypoints	keypoints detected on the image.
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 [133] .

Note

In Python API, types are given as cv2.FAST_FEATURE_DETECTOR_TYPE_5_8, cv2.FAST_FEATURE_DETECTOR_TYPE_7_12 and cv2.FAST_FEATURE_DETECTOR_TYPE_9_16. For corner detection, use cv2.FAST.detect() method.