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2015-08-04 06:36:33 -0600 commented question Handling motion-blur problem in ArUco augmented reality toolkit

Yes, ArUco toolkit is based on OpenCV.

2015-08-04 05:07:05 -0600 asked a question Handling motion-blur problem in ArUco augmented reality toolkit

I'm trying to handle motion-blur problem in ArUco augmented reality toolkit. The main idea of ArUco is to track a board of multiple markers in order to calculate the camera pose relative to the board. The position to which the virtual object will be rendered depends on the camera's position calculated relative to as many as possible markers. ArUco cannot detect markers when the board makes fast or sudden movements. But during that, the little number of markers it detects are located precisely without any jittering error. In pursuit of handling the motion-blur problem. I have applied meanshift-FAST algorithm and it succeeded in detecting all the markers which ArUco fails to detect. The markers which are detected by ArUco are detected with my algorithm with the same accuracy. However, the markers which are not detected with ArUco are detected with my algorithm with jittering errors.

My question is: Is my algorithm is considered an enhancement of ArUco toolkit and I have handled its motion-blur problem or not? By other words, detecting little number of markers without jittering errors is better or detecting all markers with jittering errors of the blurred ones?

Note: Jittering error means that the marker's position I calculated is not identical to the right position.

Thanks in advance.

2015-05-10 01:58:09 -0600 asked a question Which edge detection algorithm is more suitable for real-time applications (especially for detecting markers in augmented reality applications)?

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?