sysrmr's profile - activity

Thank you for your answer. I didn't have much time the last few days and will not the next few days, i had just enough time to try out your suggestion. I adapted the opencv Squares sample. Now i get 0 to max 30 detected Squares for the different images. Most of the times the squares are nearly the same. Just a little bit bigger/smaller then the chessboard. Only on a few images there is a big square detected which isn't the chessboard. Now i don't know how do minmize the output to 1 square or how to find out which of the squares is the best approx. for the chessboard. Any suggestions for that? Or will that just be solved if I play around a little bit more with the code (as soon as i have time i will).

Hi.

I want to write a program which takes a scan or photo taken from books/newspaper and detects a chessboard (like this) on it and gives a image of the chessboard (with parallel edges) as output. My idea was to detect the corners of the chessboard an then generate from this the output chessboard.

So i trained a Haar Classifier for each of the corners. For that i wrote a program in Qt to generate positive Images of the Corner (mark the corner on a image, program generates small image with corner in the middle of the image).

Now i have around 120 Images like this for each corner. I also have ~200 Negative Samples consisting of Images of Books with only printed Text, some with small images and some are just the inside of a chessboard without Corners and Edges.

So i trained the Classifier and tested the performance. About the half of the objects were found. But there where around 80.000 false positive.

I know 120/200 Samples are not much but i wanted to know if i am on the right track and the false positives will go away with more positive/negative samples. Or is there another, better way to detect the Corners of the chessboard.

Thanks.