1. Canny Edge Detection

2. 5 STEPS 5 STEPS Apply Gaussian filter to smooth the image in order to remove the noise Apply Gaussian filter to smooth the image in order to remove the noise Find the intensity gradients of the image Find the intensity gradients of the image Non-maximum suppression is an edge thinning technique. Non-maximum suppression is an edge thinning technique.edge thinningedge thinning Apply double threshold to determine potential edges Apply double threshold to determine potential edges Track edge by hysteresis: Finalize the detection of edges by suppressing all the other edges that are weak and not connected to strong edges. Track edge by hysteresis: Finalize the detection of edges by suppressing all the other edges that are weak and not connected to strong edges.

3. Gaussian Filter Gaussian Smoothing Gaussian Smoothing In 2-D, an isotropic (i.e. circularly symmetric) Gaussian has the form: In 2-D, an isotropic (i.e. circularly symmetric) Gaussian has the form:

4. Gaussian Filter The figure shows a suitable integer-valued convolution kernel that approximates a Gaussian with a σ of 1.0. The figure shows a suitable integer-valued convolution kernel that approximates a Gaussian with a σ of 1.0.

5. Intensity Gradients Example Example

6. Intensity Gradients Example Example

7. Intensity Gradients Example Example

8. Intensity Gradients OpenCV OpenCV

9. Non-maximum suppression Example Example

10. Thresholding Edges are found by thresholding the output of NONMAX_SUPRESSION Edges are found by thresholding the output of NONMAX_SUPRESSION If the threshold is too high: If the threshold is too high: Very few (none) edges Very few (none) edges High MISDETECTIONS, many gaps High MISDETECTIONS, many gaps If the threshold is too low: If the threshold is too low: Too many (all pixels) edges Too many (all pixels) edges High FALSE POSITIVES, many extra edges High FALSE POSITIVES, many extra edges

11. Thresholding Use a high threshold to start edge curves and a low threshold to continue them.

12. Edge Detection With Hysteresis Low thresholdHigh threshold Hysteresis (high and low threshold)

13. Edge Tracking Hysteresis thresholding [Canny ’86] Strong edge Weak edges Strong edges reinforce weak edges. We call a pixel an edge if it is strong. We also call a pixel an edge if it is weak but is connected to an edge. A pixel is connected to an edge if it is in a direction perpendicular to the edge normal Weak edge removed edges

14. References http://www.ics.uci.edu/~majumder/DIP/cl asses/EdgeDetect.pdf http://www.ics.uci.edu/~majumder/DIP/cl asses/EdgeDetect.pdf http://www.ics.uci.edu/~majumder/DIP/cl asses/EdgeDetect.pdf http://www.ics.uci.edu/~majumder/DIP/cl asses/EdgeDetect.pdf http://cronos.rutgers.edu/~meer/TEACH/ ADD/cannyedge.pdf http://cronos.rutgers.edu/~meer/TEACH/ ADD/cannyedge.pdf http://cronos.rutgers.edu/~meer/TEACH/ ADD/cannyedge.pdf http://cronos.rutgers.edu/~meer/TEACH/ ADD/cannyedge.pdf http://www.cs.hunter.cuny.edu/~ioannis/ CSc83020_lec06_EdgeDetection2.ppt http://www.cs.hunter.cuny.edu/~ioannis/ CSc83020_lec06_EdgeDetection2.ppt http://www.cs.hunter.cuny.edu/~ioannis/ CSc83020_lec06_EdgeDetection2.ppt http://www.cs.hunter.cuny.edu/~ioannis/ CSc83020_lec06_EdgeDetection2.ppt http://homepages.inf.ed.ac.uk/rbf/HIPR2/ gsmooth.htm http://homepages.inf.ed.ac.uk/rbf/HIPR2/ gsmooth.htm http://homepages.inf.ed.ac.uk/rbf/HIPR2/ gsmooth.htm http://homepages.inf.ed.ac.uk/rbf/HIPR2/ gsmooth.htm