void goodFeaturesToTrack( InputArray image, OutputArray corners,
                                     int maxCorners, double qualityLevel, double minDistance,
                                     InputArray mask = noArray(), int blockSize = 3,
                                     bool useHarrisDetector = false, double k = 0.04

void goodFeaturesToTrack( InputArray image, OutputArray corners,
                                     int maxCorners, double qualityLevel, double minDistance,
                                     InputArray mask, int blockSize,
                                     int gradientSize, bool useHarrisDetector = false,
                                     double k = 0.04 );

The function finds the most prominent corners in the image or in the specified image region.

-Function calculates the corner quality measure at every source image pixel using the #cornerMinEigenVal or #cornerHarris .
-Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are retained).
-The corners with the minimal eigenvalue less than \f$\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\f$ are rejected.
-The remaining corners are sorted by the quality measure in the descending order.
-Function throws away each corner for which there is a stronger corner at a distance less than maxDistance.

The function can be used to initialize a point-based tracker of an object.

@note If the function is called with different values A and B of the parameter qualityLevel , and A > B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector with qualityLevel=B .

@param image Input 8-bit or floating-point 32-bit, single-channel image.
@param corners Output vector of detected corners.
@param maxCorners Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set and all detected corners are returned.
@param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
@param minDistance Minimum possible Euclidean distance between the returned corners.
@param mask Optional region of interest. If the image is not empty (it needs to have the type CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
@param blockSize Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs .
@param useHarrisDetector Parameter indicating whether to use a Harris detector (see #cornerHarris) or #cornerMinEigenVal.
@param k Free parameter of the Harris detector.

@sa  cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,
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