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cs682-canny-edges

# cs682-canny-edges - Image Features Edges Formal Design of...

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1 Image Features Edges 2/11/2004 Octavia I. Camps 2 Formal Design of an Optimal Edge Detector Edge detection involves 3 steps: – Noise smoothing – Edge enhancement – Edge localization J. Canny formalized these steps to design an optimal edge detector

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2 2/11/2004 Octavia I. Camps 3 Canny Edge Detector Experiments consistently show that it performs very well Probably, the most used by C.V. practitioners 2/11/2004 Octavia I. Camps 4 Canny Edge Detector Uses a mathematical model of the edge and the noise Formalizes a performance criteria Synthesizes the best filter
3 2/11/2004 Octavia I. Camps 5 Edge Model (1D) An ideal edge can be modeled as an step A < = 0 if 0 if 0 ) ( x A x x G 2/11/2004 Octavia I. Camps 6 Performance Criteria (1) Good detection – The filter must have a stronger response at the edge location (x=0) than to noise

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4 2/11/2004 Octavia I. Camps 7 Performance Criteria (2) Good Localization – The filter response must be maximum very close to x=0 X=0 X=0 2/11/2004 Octavia I. Camps 8 Performance Criteria (3) Low False Positives – There should be only one maximum in a reasonable neighborhood of x=0 large
5 2/11/2004 Octavia I. Camps 9 Canny Edge Detector Canny found a linear, continuous filter that maximized the three given criteria. There is no close-form solution for the optimal filter. However, it looks VERY SIMILAR to the derivative of a Gaussian. 2/11/2004 Octavia I. Camps 10 Algorithm CANNY_ENHANCER The input is image I; G is a zero mean Gaussian filter (std = σ ) 1. J = I * G (smoothing) 2. For each pixel (i,j): (edge enhancement) Compute the image gradient » J(i,j) = (J x (i,j),J y (i,j))’

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cs682-canny-edges - Image Features Edges Formal Design of...

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