Canny
The Canny module implements the Canny Edge Detector first specified by John Canny is his paper
"A Computational Approach to Edge Detection". The Canny edge detector is regarded as one of the
best edge detectors currently in use. The basic algorithm can be outlined as follows:
1. Blur the image slightly. This removes the effects of random black or
white pixels (i.e. noise pixels) that can adversely affect the following stages.
Blurring is typically accomplished using a Gaussian Blur.
However, you should be able to use a box averaging filter like the Mean
filter to accomplish the same effect. The mean filter can be executed faster than a Gaussian
blur so for realtime applications the computational advantage can be worthwhile.
2. Compute the x and y derivatives of the image. This is basically an edge detection step.
Similar to the Sobel edge detector the x and y derivatives are simply
calculated by subtracting the values of the two surrounding neighboring pixels depending on
which axis is being computed (left and right for x derivative and top and bottom for y derivative).
3. From the x and y derivatives you can compute the edge magnitude which basically signifies
the strength of the edge detected at the current point.
4. If you were to look at the edge image at this point it would look very similar to the
Sobel edge filter. Namely, the edges would be thick in many areas of the resulting edge image.
To 'thin' these edges a nonmaximal filter needs to be applied that only preserves edges
that are the top or ridge of a detected edge. The nonmaximal filter is applied by first
analyzing
what the slope of the edge is (you can determine this by the sign and magnitude of the x and y
derivatives calculated in the previous steps) and use that slope to determine what direction
the current edge is heading. You can then determine the two edge magnitudes perpendicular to the
current heading to determine if the current pixel is on an edge ridge or not. If the current
pixel is on a ridge its magnitude will be greater than either of the two magnitudes
of the perpendicular pixels (think of walking on a mountain ridge with the ground decreasing
on both sides of your heading). Using this ridge detection and subsequant elimination
will result in very thin lined
edges.
5. Once the edges have been thinned the last step is to threshold the detected edges.
Edge magnitudes above the upper threshold are preserved. Edges below the upper threshold
but above the lower threshold are preserved ONLY if they connect (i.e. 8 neighborhood touching)
to edges that are above the upper threshold. This process is know as hysteresis and allows edges
to grow larger than they would by using a single threshold without introducing more noise into the
resulting edge image.
Interface
Instructions
1. Theta  Select the Gaussian blur standard deviation amount. Also known as sigma. Typical values
range from 0.60 to 2.40. The higher the value the greater the blurring of the image. Note
that the window size of the filter is determined automatically.
2. Low Threshold  Select the low threshold above which possible edges can exist. Edges below the low threshold
are removed from the image.
3. High Threshold  Select the high threshold above which edges are preserved in the image. Edges between
the high and low threshold are ONLY preserved if they are connected to an edge that is
above the high threshold. See hysteresis above.
Example
Source  Canny Edge Detection 
 
See Also
Sobel
Gaussian
Difference of Gaussian
For more information
HIPR  Canny Edge Detector
Bill Green  Canny Edge Detection Tutorial
Noah Kuntz  Canny Tutorial
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