Implements the Canny algorithm for edge detection.
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The function finds the edges on the input image image and marks them in the output image edges using the Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking, the largest value is used to find the initial segments of strong edges.
Calculates eigenvalues and eigenvectors of image blocks for corner detection.
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For every pixel, the function cvCornerEigenValsAndVecs considers a neigborhood S(p). It calcualtes the covariation matrix of derivatives over the neigborhood as:
After that it finds eigenvectors and eigenvalues of the matrix and stores them into destination image in form where
are the eigenvalues of ; not sorted
are the eigenvectors corresponding to
are the eigenvectors corresponding to
Harris edge detector.
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The function runs the Harris edge detector on the image. Similarly to CornerMinEigenVal and CornerEigenValsAndVecs , for each pixel it calculates a gradient covariation matrix over a neighborhood. Then, it stores
to the destination image. Corners in the image can be found as the local maxima of the destination image.
Calculates the minimal eigenvalue of gradient matrices for corner detection.
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The function is similar to CornerEigenValsAndVecs but it calculates and stores only the minimal eigen value of derivative covariation matrix for every pixel, i.e. in terms of the previous function.
Refines the corner locations.
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The function iterates to find the sub-pixel accurate location of corners, or radial saddle points, as shown in on the picture 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 the image gradient at the one of the points in a neighborhood of . The value of is to be found such 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 keeps within a set threshold.
Determines strong corners on an image.
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The function finds the corners with big eigenvalues in the image. The function first calculates the minimal eigenvalue for every source image pixel using the CornerMinEigenVal function and stores them in eigImage . Then it performs non-maxima suppression (only the local maxima in neighborhood are retained). The next step rejects the corners with the minimal eigenvalue less than . Finally, the function ensures that the distance between any two corners is not smaller than minDistance . The weaker corners (with a smaller min eigenvalue) that are too close to the stronger corners are rejected.
Note that the if the function is called with different values A and B of the parameter qualityLevel , and A > {B}, the array of returned corners with qualityLevel=A will be the prefix of the output corners array with qualityLevel=B .
Finds lines in a binary image using a Hough transform.
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The function implements a few variants of the Hough transform for line detection.
Example. Detecting lines with Hough transform.
/* This is a standalone program. Pass an image name as a first parameter
of the program. Switch between standard and probabilistic Hough transform
by changing "#if 1" to "#if 0" and back */
#include <cv.h>
#include <highgui.h>
#include <math.h>
int main(int argc, char** argv)
{
IplImage* src;
if( argc == 2 && (src=cvLoadImage(argv[1], 0))!= 0)
{
IplImage* dst = cvCreateImage( cvGetSize(src), 8, 1 );
IplImage* color_dst = cvCreateImage( cvGetSize(src), 8, 3 );
CvMemStorage* storage = cvCreateMemStorage(0);
CvSeq* lines = 0;
int i;
cvCanny( src, dst, 50, 200, 3 );
cvCvtColor( dst, color_dst, CV_GRAY2BGR );
#if 1
lines = cvHoughLines2( dst,
storage,
CV_HOUGH_STANDARD,
1,
CV_PI/180,
100,
0,
0 );
for( i = 0; i < MIN(lines->total,100); i++ )
{
float* line = (float*)cvGetSeqElem(lines,i);
float rho = line[0];
float theta = line[1];
CvPoint pt1, pt2;
double a = cos(theta), b = sin(theta);
double x0 = a*rho, y0 = b*rho;
pt1.x = cvRound(x0 + 1000*(-b));
pt1.y = cvRound(y0 + 1000*(a));
pt2.x = cvRound(x0 - 1000*(-b));
pt2.y = cvRound(y0 - 1000*(a));
cvLine( color_dst, pt1, pt2, CV_RGB(255,0,0), 3, 8 );
}
#else
lines = cvHoughLines2( dst,
storage,
CV_HOUGH_PROBABILISTIC,
1,
CV_PI/180,
80,
30,
10 );
for( i = 0; i < lines->total; i++ )
{
CvPoint* line = (CvPoint*)cvGetSeqElem(lines,i);
cvLine( color_dst, line[0], line[1], CV_RGB(255,0,0), 3, 8 );
}
#endif
cvNamedWindow( "Source", 1 );
cvShowImage( "Source", src );
cvNamedWindow( "Hough", 1 );
cvShowImage( "Hough", color_dst );
cvWaitKey(0);
}
}
This is the sample picture the function parameters have been tuned for:
And this is the output of the above program in the case of probabilistic Hough transform ( #if 0 case):
Calculates the feature map for corner detection.
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The function calculates the function
where denotes one of the first image derivatives and denotes a second image derivative.
The corners can be found as local maximums of the function below:
// assume that the image is floating-point
IplImage* corners = cvCloneImage(image);
IplImage* dilated_corners = cvCloneImage(image);
IplImage* corner_mask = cvCreateImage( cvGetSize(image), 8, 1 );
cvPreCornerDetect( image, corners, 3 );
cvDilate( corners, dilated_corners, 0, 1 );
cvSubS( corners, dilated_corners, corners );
cvCmpS( corners, 0, corner_mask, CV_CMP_GE );
cvReleaseImage( &corners );
cvReleaseImage( &dilated_corners );
Reads the raster line to the buffer.
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The function implements a particular application of line iterators. The function reads all of the image points lying on the line between pt1 and pt2 , including the end points, and stores them into the buffer.