Why is the Canny edge detection in MATLAB different to OpenCV?
When I am comparing the outputs of using the “edge” function in MATLAB to the “Canny” function in OpenCV, I notice that the OpenCV implementation filters out more edges compared to the MATLAB’s implementation.
In MATLAB, I use:
>> edge(I, 'canny', [], 2.0)In OpenCV (C++), I use:
>> cv::Canny(gray_image_smooth, edge, lowThresh, highThresh);where I have tried to set the low and high thresholds using a manner similar to what is given in the “edge” function.
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The ‘canny’ option in the MATLAB edge function attempts to be as precisely faithful as possible to the algorithm described in Canny’s Masters Thesis and PAMI paper:
There are several notable differences between the algorithm in “edge” and what appears in OpenCV:
1) Smoothing of input image prior to computation of gradients.
We use a 16x16 gaussian filter with sigma sqrt(2) prior to computing gradients. OpenCV does not do this step.
2) In the gradient computation step, we use a Derivative of Gaussian filter. OpenCV uses the Sobel operator.
3) Our non-maximum suppression step is different than that of OpenCV and is faithful to the algorithm described in the Canny paper.
4) Determining hysteresis threshold limits
We do this using a histogram approach, where the gradient magnitude is binned into 64 levels, and the threshold is based on a percentile of the gradient values (the 70% level is the high threshold). 28% (0.4 of the 70%) is the level for the lower threshold. So there are two levels: Any pixel in the top 30% of the gradient (which passes the non-maximum suppression step) is definitely an edge pixel. The pixels between high and low threshold are included in another list for the next step OpenCV does not have a mechanism for determining the hysteresis threshold limits automatically
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