Feature Detection and Description

Note

• An example explaining keypoint detection and description can be found at opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp

FAST

Detects corners using the FAST algorithm

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

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

Python: cv2.FastFeatureDetector([threshold[, nonmaxSuppression]]) → <FastFeatureDetector object>

Python: cv2.FastFeatureDetector(threshold, nonmaxSuppression, type) → <FastFeatureDetector object>

Python: cv2.FastFeatureDetector.detect(image[, mask]) → keypoints

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 [Rosten06].

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.

[Rosten06]

Rosten. Machine Learning for High-speed Corner Detection, 2006.

MSER

class MSER : public FeatureDetector

Maximally stable extremal region extractor.

class MSER : public CvMSERParams
{
public:
    // default constructor
    MSER();
    // constructor that initializes all the algorithm parameters
    MSER( int _delta, int _min_area, int _max_area,
          float _max_variation, float _min_diversity,
          int _max_evolution, double _area_threshold,
          double _min_margin, int _edge_blur_size );
    // runs the extractor on the specified image; returns the MSERs,
    // each encoded as a contour (vector<Point>, see findContours)
    // the optional mask marks the area where MSERs are searched for
    void operator()( const Mat& image, vector<vector<Point> >& msers, const Mat& mask ) const;
};

The class encapsulates all the parameters of the MSER extraction algorithm (see http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions). Also see http://code.opencv.org/projects/opencv/wiki/MSER for useful comments and parameters description.

Note

• (Python) A complete example showing the use of the MSER detector can be found at opencv_source_code/samples/python2/mser.py

ORB

class ORB : public Feature2D

Class implementing the ORB (oriented BRIEF) keypoint detector and descriptor extractor, described in [RRKB11]. The algorithm uses FAST in pyramids to detect stable keypoints, selects the strongest features using FAST or Harris response, finds their orientation using first-order moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or k-tuples) are rotated according to the measured orientation).

[RRKB11]

Ethan Rublee, Vincent Rabaud, Kurt Konolige, Gary R. Bradski: ORB: An efficient alternative to SIFT or SURF. ICCV 2011: 2564-2571.

ORB::ORB

The ORB constructor

C++: ORB::ORB(int nfeatures=500, float scaleFactor=1.2f, int nlevels=8, int edgeThreshold=31, int firstLevel=0, int WTA_K=2, int scoreType=ORB::HARRIS_SCORE, int patchSize=31)

Python: cv2.ORB([nfeatures[, scaleFactor[, nlevels[, edgeThreshold[, firstLevel[, WTA_K[, scoreType[, patchSize]]]]]]]]) → <ORB object>

Parameters:

• nfeatures – The maximum number of features to retain.
• scaleFactor – Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor will mean that to cover certain scale range you will need more pyramid levels and so the speed will suffer.
• nlevels – The number of pyramid levels. The smallest level will have linear size equal to input_image_linear_size/pow(scaleFactor, nlevels).
• edgeThreshold – This is size of the border where the features are not detected. It should roughly match the patchSize parameter.
• firstLevel – It should be 0 in the current implementation.
• WTA_K – The number of points that produce each element of the oriented BRIEF descriptor. The default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 random points (of course, those point coordinates are random, but they are generated from the pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
• scoreType – The default HARRIS_SCORE means that Harris algorithm is used to rank features (the score is written to KeyPoint::score and is used to retain best nfeatures features); FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, but it is a little faster to compute.
• patchSize – size of the patch used by the oriented BRIEF descriptor. Of course, on smaller pyramid layers the perceived image area covered by a feature will be larger.

ORB::operator()

Finds keypoints in an image and computes their descriptors

C++: void ORB::operator()(InputArray image, InputArray mask, vector<KeyPoint>& keypoints, OutputArray descriptors, bool useProvidedKeypoints=false ) const

Python: cv2.ORB.detect(image[, mask]) → keypoints

Python: cv2.ORB.compute(image, keypoints[, descriptors]) → keypoints, descriptors

Python: cv2.ORB.detectAndCompute(image, mask[, descriptors[, useProvidedKeypoints]]) → keypoints, descriptors

Parameters:

• image – The input 8-bit grayscale image.
• mask – The operation mask.
• keypoints – The output vector of keypoints.
• descriptors – The output descriptors. Pass cv::noArray() if you do not need it.
• useProvidedKeypoints – If it is true, then the method will use the provided vector of keypoints instead of detecting them.

BRISK

class BRISK : public Feature2D

Class implementing the BRISK keypoint detector and descriptor extractor, described in [LCS11].

[LCS11]

Stefan Leutenegger, Margarita Chli and Roland Siegwart: BRISK: Binary Robust Invariant Scalable Keypoints. ICCV 2011: 2548-2555.

BRISK::BRISK

The BRISK constructor

C++: BRISK::BRISK(int thresh=30, int octaves=3, float patternScale=1.0f)

Python: cv2.BRISK([thresh[, octaves[, patternScale]]]) → <BRISK object>

Parameters:

• thresh – FAST/AGAST detection threshold score.
• octaves – detection octaves. Use 0 to do single scale.
• patternScale – apply this scale to the pattern used for sampling the neighbourhood of a keypoint.

BRISK::BRISK

The BRISK constructor for a custom pattern

C++: BRISK::BRISK(std::vector<float>& radiusList, std::vector<int>& numberList, float dMax=5.85f, float dMin=8.2f, std::vector<int> indexChange=std::vector<int>())

Python: cv2.BRISK(radiusList, numberList[, dMax[, dMin[, indexChange]]]) → <BRISK object>

Parameters:

• radiusList – defines the radii (in pixels) where the samples around a keypoint are taken (for keypoint scale 1).
• numberList – defines the number of sampling points on the sampling circle. Must be the same size as radiusList..
• dMax – threshold for the short pairings used for descriptor formation (in pixels for keypoint scale 1).
• dMin – threshold for the long pairings used for orientation determination (in pixels for keypoint scale 1).
• indexChanges – index remapping of the bits.

BRISK::operator()

Finds keypoints in an image and computes their descriptors

C++: void BRISK::operator()(InputArray image, InputArray mask, vector<KeyPoint>& keypoints, OutputArray descriptors, bool useProvidedKeypoints=false ) const

Python: cv2.BRISK.detect(image[, mask]) → keypoints

Python: cv2.BRISK.compute(image, keypoints[, descriptors]) → keypoints, descriptors

Python: cv2.BRISK.detectAndCompute(image, mask[, descriptors[, useProvidedKeypoints]]) → keypoints, descriptors

Parameters:

• image – The input 8-bit grayscale image.
• mask – The operation mask.
• keypoints – The output vector of keypoints.
• descriptors – The output descriptors. Pass cv::noArray() if you do not need it.
• useProvidedKeypoints – If it is true, then the method will use the provided vector of keypoints instead of detecting them.

KAZE

class KAZE : public Feature2D

Class implementing the KAZE keypoint detector and descriptor extractor, described in [ABD12].

class CV_EXPORTS_W KAZE : public Feature2D
{
public:
    CV_WRAP KAZE();
    CV_WRAP explicit KAZE(bool extended, bool upright, float threshold = 0.001f,
                          int octaves = 4, int sublevels = 4, int diffusivity = DIFF_PM_G2);
};

Note

AKAZE descriptor can only be used with KAZE or AKAZE keypoints

[ABD12]

KAZE Features. Pablo F. Alcantarilla, Adrien Bartoli and Andrew J. Davison. In European Conference on Computer Vision (ECCV), Fiorenze, Italy, October 2012.

KAZE::KAZE

The KAZE constructor

C++: KAZE::KAZE(bool extended, bool upright, float threshold, int octaves, int sublevels, int diffusivity)

Parameters:

• extended – Set to enable extraction of extended (128-byte) descriptor.
• upright – Set to enable use of upright descriptors (non rotation-invariant).
• threshold – Detector response threshold to accept point
• octaves – Maximum octave evolution of the image
• sublevels – Default number of sublevels per scale level
• diffusivity – Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or DIFF_CHARBONNIER

AKAZE

class AKAZE : public Feature2D

Class implementing the AKAZE keypoint detector and descriptor extractor, described in [ANB13].

class CV_EXPORTS_W AKAZE : public Feature2D
{
public:
    CV_WRAP AKAZE();
    CV_WRAP explicit AKAZE(int descriptor_type, int descriptor_size = 0, int descriptor_channels = 3,
                           float threshold = 0.001f, int octaves = 4, int sublevels = 4, int diffusivity = DIFF_PM_G2);
};

Note

AKAZE descriptor can only be used with KAZE or AKAZE keypoints

[ANB13]

Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces. Pablo F. Alcantarilla, Jesús Nuevo and Adrien Bartoli. In British Machine Vision Conference (BMVC), Bristol, UK, September 2013.

AKAZE::AKAZE

The AKAZE constructor

C++: AKAZE::AKAZE(int descriptor_type, int descriptor_size, int descriptor_channels, float threshold, int octaves, int sublevels, int diffusivity)

Parameters:

• descriptor_type – Type of the extracted descriptor: DESCRIPTOR_KAZE, DESCRIPTOR_KAZE_UPRIGHT, DESCRIPTOR_MLDB or DESCRIPTOR_MLDB_UPRIGHT.
• descriptor_size – Size of the descriptor in bits. 0 -> Full size
• descriptor_channels – Number of channels in the descriptor (1, 2, 3)
• threshold – Detector response threshold to accept point
• octaves – Maximum octave evolution of the image
• sublevels – Default number of sublevels per scale level
• diffusivity – Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or DIFF_CHARBONNIER

SIFT

class SIFT : public Feature2D

The SIFT algorithm has been moved to opencv_contrib/xfeatures2d module.