Feature Detection and Description

FAST

Detects corners using the FAST algorithm

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

Parameters:

• image – Image where keypoints (corners) are detected.
• keypoints – Keypoints detected on the image.
• threshold – Threshold on difference between intensity of the central pixel and pixels on a circle around this pixel. See the algorithm description below.
• nonmaxSupression – If it is true, non-maximum suppression is applied to detected corners (keypoints).

Detects corners using the FAST algorithm by [Rosten06].

[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://opencv.willowgarage.com/wiki/documentation/cpp/features2d/MSER for useful comments and parameters description.

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=HARRIS_SCORE, int patchSize=31)

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

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.