问题
I'm attempting to implement an easter egg in a mobile app I'm working on. These easter egg will be triggered when a logo is detected in the camera view. The logo I'm trying to detect is this one:
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I'm not quite sure what the best way to approach this is as I'm pretty new to computer vision. I'm currently finding horizontal edges using the Canny algorithm. I then find line segments using the probabilistic Hough transform. The output of this looks as follows (blue lines represent the line segments detected by the probabilistic Hough transform):
The next step I was going to take would be to look for a group of around 24 lines (fitting within a nearly square rectangle), each line would have to be approximately the same length. I'd use these two signals to indicate the potential presence of the logo. I realise that this is probably a very naive approach and would welcome suggestions as to how to better detect this logo in a more reliable manner?
Thanks
回答1:
You may want to go with SIFT using Rob Hess' SIFT Library. It's using OpenCV and also pretty fast. I guess that easier than your current way of approaching the logo detection :)
Try also looking for SURF, which claims to be faster & robuster than SIFT. This Feature Detection tutorial will help you.
回答2:
You may just want to use LogoGrab's technology. It's the best out there and offers all sorts of APIs (both mobile and HTTP). http://www.logograb.com/technologyteam/
回答3:
I'm not quite sure if you would find such features in the logo to go with a SIFT/SURF approach. As an alternative you can try training a Haar-like feature classifier and use it for detecting the logo, just like opencv does for face detection.
来源：https://stackoverflow.com/questions/9748900/logo-detection-using-opencv