cornerSubPix函数英文介绍



本文转自：http://blog.csdn.net/mysunnnyday/article/details/9423611

cornerSubPix

Refines the corner locations.

C++:  void  cornerSubPix (InputArray  image, InputOutputArray  corners, Size  winSize, Size  zeroZone, TermCriteria  criteria )

Python:   cv2. cornerSubPix (image, corners, winSize, zeroZone, criteria ) → None

C:  void  cvFindCornerSubPix (const CvArr*  image, CvPoint2D32f*  corners, int  count, CvSize  winSize, CvSize  zeroZone, CvTermCriteria  criteria )

Python:   cv. FindCornerSubPix (image, corners, winSize, zeroZone, criteria ) → corners ¶

Parameters:

image – Input image. corners – Initial coordinates of the input corners and refined coordinates provided for output. winSize – Half of the side length of the search window. For example, if winSize=Size(5,5) , then a  search window is used. zeroZone – Half of the size of the dead region in the middle of the search zone over which the summation in the formula below is not done. It is used sometimes to avoid possible singularities of the autocorrelation matrix. The value of (-1,-1) indicates that there is no such a size. criteria – Criteria for termination of the iterative process of corner refinement. That is, the process of corner position refinement stops either after criteria.maxCountiterations or when the corner position moves by less than criteria.epsilon on some iteration.

The function iterates to find the sub-pixel accurate location of corners or radial saddle points, as shown on the figure below.

Sub-pixel accurate corner locator is based on the observation that every vector from the center  to a point  located within a neighborhood of  is orthogonal to the image gradient at  subject to image and measurement noise. Consider the expression:

where  is an image gradient at one of the points  in a neighborhood of  . The value of  is to be found so that  is minimized. A system of equations may be set up with  set to zero:

where the gradients are summed within a neighborhood (“search window”) of  . Calling the first gradient term  and the second gradient term  gives:

The algorithm sets the center of the neighborhood window at this new center  and then iterates until the center stays within a set threshold.