Thanks Petr, Thanks Thomas, Very interesting report.
I do something similar in kifu-snap. Instead of RANSAC, I use another Hough Transform to detect the sinusoids in the first Hough Transform. I also use k-means color clustering for detecting stones, but also combine this with other features (like circle detection). So kifu-snap should work with black-and-white diagrams, for instance. I used a lot of tricks to make my implementation fast (because it has to be fast on a phone), so it takes only 0.1 second on average to process one picture on one core of my 2.4 GHz core2 duo notebook. Your dataset is way too easy, though. I tried only a few pics, and kifu-snap had no difficulty with them. I uploaded my own dataset there: http://remi.coulom.free.fr/kifu-snap/goban.tar.bz2 I don’t own the rights for most of these photos. Found most of them with Google images. If anybody complains, I will remove the file. The .ks files are coordinates of the corners of the board (pixels, with 0,0 at the center of the image, IIRC). The .gob files are stone configurations. This is the result of stone-recognition (given the correct grid placement from the .ks file): http://remi.coulom.free.fr/kifu-snap/stone-recognition.txt (time is in milliseconds) Only 32/99 grids placements are recognized correctly. Most frequent mistake is to be off by one or two lines in one direction. Only 14/99 are fully recognized automatically. I admit some of them are particularly tricky. But I also have plenty of pictures from real usage of kifu-snap, and it is still difficult to be correct. When I use kifu-snap at the Go club or in a tournament, I usually get a 100% automatically recognized board a bit more than 50% of the time (completely biased guessed rate). Automatic Go board recognition is a really difficult and fun challenge. Rémi On 12 août 2014, at 13:35, Petr Baudis <[email protected]> wrote: > Hi! > > Tomas Musil (a student of mine), has created a state-of-the-art open > source Go board optical recognition software. We have focused on > completely automatic runs, so it automatically detects the board corners > and then the stones on the board, and the precision seems pretty good > at least in reasonable lighting conditions. > > You can find it at http://tomasm.cz/imago together with a lot of > pictures, documentation and bachelor thesis describing the algorithms > in detail. In the thesis, Tomas also compares it against other similar > apps, and it appears Imago shows the best performance of all these > that were available to us. > > Unfortunately, we specifically couldn't easily compare it to Remi > Coulom's Kifu-snap for multiple reasons - mainly because that is > a mobile app. Hopefully, someone will be able to compare these two > in the future. At any rate, I think Imago is a great starting point > for anyone who would like to play with Go board recognition. > > > My personal dream would be if we added video capability and further > improved speed + reliability in time for EGC2015 (in Czech Republic) > and were able to deploy it there to transfer large number of top boards. > But this will depend on how much time Tomas will have after the summer > (and we didn't actually check with EGC2015 organizers yet), so it's > still more of just a dream. :-) > > Petr Baudis > _______________________________________________ > Computer-go mailing list > [email protected] > http://dvandva.org/cgi-bin/mailman/listinfo/computer-go _______________________________________________ Computer-go mailing list [email protected] http://dvandva.org/cgi-bin/mailman/listinfo/computer-go
