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
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C++: void FAST(InputArray image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSuppression=true )
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C++: void FAST(InputArray image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSuppression, int type)
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
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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.
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MSER
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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 detectRegions( InputArray image, vector<vector<Point> >& msers, vector<Rect>& bboxes ) 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
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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
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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)
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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.
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ORB::operator()
Finds keypoints in an image and computes their descriptors
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C++: void ORB::operator()(InputArray image, InputArray mask, vector<KeyPoint>& keypoints, OutputArray descriptors, bool useProvidedKeypoints=false ) const
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Python: cv2.ORB.detect(image[, mask]) → keypoints
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Python: cv2.ORB.compute(image, keypoints[, descriptors]) → keypoints, descriptors
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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.
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BRISK
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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
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C++: BRISK::BRISK(int thresh=30, int octaves=3, float patternScale=1.0f)
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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.
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BRISK::BRISK
The BRISK constructor for a custom pattern
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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>())
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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.
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BRISK::operator()
Finds keypoints in an image and computes their descriptors
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C++: void BRISK::operator()(InputArray image, InputArray mask, vector<KeyPoint>& keypoints, OutputArray descriptors, bool useProvidedKeypoints=false ) const
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Python: cv2.BRISK.detect(image[, mask]) → keypoints
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Python: cv2.BRISK.compute(image, keypoints[, descriptors]) → keypoints, descriptors
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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.
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KAZE
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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
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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
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AKAZE
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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 descriptors can only be used with KAZE or AKAZE keypoints. Try to avoid using extract and detect instead of operator() due to performance reasons.
[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
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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
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SIFT
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class SIFT : public Feature2D
The SIFT algorithm has been moved to opencv_contrib/xfeatures2d module.
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