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, nonmaximum suppression is applied to detected corners (keypoints).

Detects corners using the FAST algorithm by [Rosten06].
[Rosten06] 
 Rosten. Machine Learning for Highspeed 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 firstorder moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or ktuples) 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: 25642571. 
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 predefined 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 8bit 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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