Which edge detection algorithm is more suitable for real-time applications (especially for detecting markers in augmented reality applications)?

asked 2015-05-09 17:51:27 -0500

I'm trying to implement an augmented reality application. I want to use a suitable edge detection method to detect borders of markers found in the real time scene. The edge detector should be robust to reduce false positive rate and fast to reduce consuming time to be suitable for real time application.

I know that Canny edge detector is the most commonly used and it is really robust, but it is very slow.

I was thinking about using SUSAN edge detector, but I don't know if it is more or less robust and fast than Canny.

I was thinking also about using SURF feature extractor to detect borders of markers, but I don't know if it is faster or slower than Canny edge detector.

Anyone can help me to choose the most suitable method to detect borders of markers in my real-time application?

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hmm, SUSAN is not built into opencv, unfortunately, but Sobel() is missing in your list.

SURF or SIFT will not nessecarily detect corners (or edges), more like locations, they deem to be a good 'keypoint' rather have a look at cornerHarris() in that case.

berak gravatar imageberak ( 2015-05-10 02:27:36 -0500 )edit