Question

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: .

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

Answer 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.