Determines strong corners on an image

corners = cv.goodFeaturesToTrack(image)
corners = cv.goodFeaturesToTrack(image, 'OptionName', optionValue, ...)

Input

• image Input 8-bit or floating-point 32-bit, single-channel image.

Output

• corners Output vector of detected corners. A cell array of 2-elements vectors {[x,y], ...}.

Options

• MaxCorners Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. default 1000. MaxCorners <= 0 implies that no limit on the maximum is set and all detected corners are returned.
• 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 cv.cornerMinEigenVal) or the Harris function response (see cv.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. default 0.01
• MinDistance Minimum possible Euclidean distance between the returned corners. default 2.0
• Mask Optional region of interest. If the image is not empty (it needs to have the type uint8/logical and the same size as image), it specifies the region in which the corners are detected. It is not set by default.
• BlockSize Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cv.cornerEigenValsAndVecs. default 3
• GradientSize Gradient window size to use on the input. default 3
• UseHarrisDetector Parameter indicating whether to use a Harris detector (see cv.cornerHarris) or cv.cornerMinEigenVal, default false
• K Free parameter of the Harris detector. default 0.04

The function finds the most prominent corners in the image or in the specified image region, as described in [Shi94]:

1. Function calculates the corner quality measure at every source image pixel using the cv.cornerMinEigenVal or cv.cornerHarris.
2. Function performs a non-maximum suppression (the local maximums in 3x3 neighborhood are retained).
3. The corners with the minimal eigenvalue less than QualityLevel * max_{x,y}(qualityMeasureMap(x,y)) are rejected.
4. The remaining corners are sorted by the quality measure in the descending order.
5. 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.

References

[Shi94]:

Jianbo Shi and Carlo Tomasi. Good features to track. In Computer Vision and Pattern Recognition, 1994. Proceedings CVPR'94., 1994 IEEE Computer Society Conference on, pages 593-600. IEEE, 1994.