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Canny Edge Detection in image processing
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- 2. • Our goalis to extract a “line drawing” representation from an image • Useful for recognition: edges contain shape information – invariance
- 3. Derivatives • Edges arelocations with high image gradient or derivative • Estimate derivative using finite difference • Problem?
- 4. Smoothing • Reduce imagenoise by smoothing with a Gaussian
- 5. Edge orientation • Wouldlike gradients in all directions • Approximate: – Compute smoothed derivatives in x,y directions – Edge strength – Edge normal
- 6. Canny Edge Detection •Compute edge strength and orientation at all pixels • “Non-max suppression” – Reduce thick edge strength responses around true edges • Link and threshold using “hysteresis” – Simple method of “contour completion”
- 7. Non-maximum suppression: Select thesingle maximum point across the width of an edge. Slides by D. Lowe
- 8. Non-maximum suppression At q, the valuemust be larger than values interpolated at p or r.
- 9. Examples: Non-Maximum Suppression courtesy ofG. Loy Original image Gradient magnitude Non-maxima suppressed Slide credit: Christopher Rasmussen
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- 14. Linking to the nextedge point Assume the marked point is an edge point. Take the normal to the gradient at that point and use this to predict continuation points (either r or s).
- 15. Edge Hysteresis • Hysteresis:A lag or momentum factor • Idea: Maintain two thresholds khigh and klow –Use khigh to find strong edges to start edge chain –Use klow to find weak edges which continue edge chain • Typical ratio of thresholds is roughly khigh / klow = 2
- 16. Example: Canny EdgeDetection courtesy of G. Loy gap is gone Original image Strong edges only Strong + connected weak edges Weak edges
- 17. Problem? • Texture – Cannyedge detection responds all over textured regions