2013-02-11 09:21:04 -0600 | received badge | ● Scholar (source) |
2013-02-11 09:20:58 -0600 | commented answer | Running into an error using cv::BFMatcher (bruteforce matcher) with 10,100 training images Very strange. This must be it. I'm currently using the windows superpack, so I guess I'll have to go recompile my own opencv binaries, but what you wrote looks reasonable. It seems like such an odd requirement. In the meantime, I wrote my own brute-force matcher and it has no such requirements. I'm not sure why that assertion would exist. Thanks |
2013-02-11 09:18:42 -0600 | received badge | ● Supporter (source) |
2013-02-11 09:18:24 -0600 | commented question | Running into an error using cv::BFMatcher (bruteforce matcher) with 10,100 training images @Guanta You can cluster binary features with k-means, using hamming distance as the distance metric (you can't do that with OpenCV's k-means, but it's pretty easy to code yourself). I'm trying a few different approaches out |
2013-02-08 07:36:58 -0600 | received badge | ● Editor (source) |
2013-02-07 13:22:17 -0600 | asked a question | Running into an error using cv::BFMatcher (bruteforce matcher) with 10,100 training images I'm implementing a simple bag-of-words matching system, where I have Essentially, I put all Finally, given a new The error I get is the following:
My question is simple, what is going on and how can I make this work? It's pretty clear that for some reason it considers |