Adjusting the threshold in Canny edge algorithm
Canny edge detector is a multi-step detector using hysteresis thresholding (it uses two threshold instead of one), and edge tracking (your last snippet is the part of this step). I suggest reading the wikipedia entry first. One possible solution could be to choose the high threshold, so e.g. 70% of the image pixels would be classified as edge (initially - you could do this quickly using histograms), than choose the low threshold as e.g. 40% of the high threshold. It might be a good idea to try to perform edge detection on image block rather than the whole image, so your algorithm could calculate different thresholds for different areas.
Note that CAPTCHA-s are designed to be hard to segment, and adding noise that broke edge detection is one technique to achive this (you might need to smooth the image first).
Gambit King
Updated on June 27, 2022Comments
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Gambit King almost 2 years
I wanted to try my hand at text recognition, so i've used opencv to trace out the edges and c++ to find slopes, curves etc, the edge algorithm works well on big and uncluttered sets of characters but when it comes against small printed text or text with a lot of background noise like embedded in captcha it struggles and looks incomplete, my guess was i hadn't set the threshold values correctly and tried different values with no success.
Here is my code :
#include "cv.h" #include "highgui.h" using namespace cv; const int low_threshold = 50; const int high_threshold = 150; int main() { IplImage* newImg; IplImage* grayImg; IplImage* cannyImg; newImg = cvLoadImage("ocv.bmp",1); grayImg = cvCreateImage( cvSize(newImg->width, newImg->height), IPL_DEPTH_8U, 1 ); cvCvtColor( newImg, grayImg, CV_BGR2GRAY ); cannyImg = cvCreateImage(cvGetSize(newImg), IPL_DEPTH_8U, 1); cvCanny(grayImg, cannyImg, low_threshold, high_threshold, 3); cvNamedWindow ("Source", 1); cvNamedWindow ("Destination",1); cvShowImage ("Source", newImg ); cvShowImage ("Destination", cannyImg ); cvWaitKey(0); cvDestroyWindow ("Source" ); cvDestroyWindow ("Destination" ); cvReleaseImage (&newImg ); cvReleaseImage (&grayImg ); cvReleaseImage (&cannyImg ); return 0; }
I've looked across the net and have seen some complicated thresholding conditions like in this code from this site :
% Set direction to either 0, 45, -45 or 90 depending on angle. [x,y]=size(f1); for i=1:x-1, for j=1:y-1, if ((gradAngle(i,j)>67.5 && gradAngle(i,j)<=90) || (gradAngle(i,j)>=-90 && gradAngle(i,j)<=-67.5)) gradDirection(i,j)=0; elseif ((gradAngle(i,j)>22.5 && gradAngle(i,j)<=67.5)) gradDirection(i,j)=45; elseif ((gradAngle(i,j)>-22.5 && gradAngle(i,j)<=22.5)) gradDirection(i,j)=90; elseif ((gradAngle(i,j)>-67.5 && gradAngle(i,j)<=-22.5)) gradDirection(i,j)=-45; end end end
If this is the solution can somebody provide me the c++ equivalent of this algorithm, if it's not what else can i do ?