Edge Detection


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Edge Detection

  1. 1. Edge Detection
  2. 2. What is Edge Detection? Identifying points/Edges in a digital image at which the image brightness changes sharply or has discontinuities. - Edges are significant local changes of intensity in an image. - Edges typically occur on the boundary between two different regions in an image.
  3. 3. Goal of edge detection Edge detection is extensively used in image segmentation when we want to divide the image into areas corresponding to different objects. If we need to extract different object from an image, we need Edge Detection Using Edge Detection, we can: - Produce a line drawing of a scene from an image of that scene. - Important features can be extracted from the edges of an image (e.g., corners, lines, curves). - These features are used by higher-level computer vision algorithms (e.g., recognition, Image comparizon ). Unaccepted object can be remove.
  4. 4. Process of Edge Detection Most of edge detection algorithm are based on one of two basic properties of intensity values: Discontinuity and similarity. Typically, there are three steps to perform edge detection: 1. Noise reduction 2. Edge enhancement 3. Edge localization
  5. 5. Process of Edge Detection (...) Noise reduction where we try to suppress as much noise as possible, without smoothing away the meaningful edges. Original Image After Nois Reduction
  6. 6. Process of Edge Detection (...) Edge enhancement where we apply some kind of filter that responds strongly at edges and weakly elsewhere, so that the edges may be identified as local maxima in the filter’s output . One suggestion is to use some kind of high pass filter.
  7. 7. Process of Edge Detection (...) Edge localization where we decide which of the local maxima output by the filter are meaningful edges and which are caused by noise
  8. 8. Process of Edge Detection (...) There are many algorithm for Edge Detection. Some are: Robert's edge detector Prewitt edge detector Sobel edge detector Frie Chen edge detector Canny edge detector Canny edge detector is giving best output, I am going to explain Canny edge detector.
  9. 9. Canny Edge Detector Canny Edge Detector is complex and uses a multi-stage algorithm to detect a wide range of edges in images. It is most commonly implemented edge detection algorithm. It has three basic objectives: Low error rate Edge points should be well localized Single edge point response
  10. 10. Canny Edge Detector As I mention before, canny edge detector have multiple algorithm. It have 5 steps, those are: Image Smoothing Gradient Operation Nonmaxima Suppression Hysteresis Thresholding Connectivity Analysis
  11. 11. Canny Edge Detector Image Smoothing Reduce image noise by smoothing with a Gaussian The choice of σ depends on desired behavior large σ detects large scale edges small σ detects fine features The larger the width of the Gaussian mask, the lower is the detector's sensitivity to noise.
  12. 12. Canny Edge Detector
  13. 13. Canny Edge Detector Nonmaxima Suppression Nonmaxima Suppression reduce thick edge strength responses around true edges select the single maximum point across the width of an edge. is used to trace along the edge in the edge direction and suppress any pixel value (sets it equal to 0) that is not considered to be an edge. This will give a thin line in the output image.
  14. 14. Canny Edge Detector Hysteresis Thresholding Large intensity gradients are more likely to correspond to edges than small intensity gradients. It is in most cases impossible to specify a threshold at which a given intensity gradient switches from corresponding to an edge into not doing so. Therefore Canny uses thresholding with hysteresis. Thresholding with hysteresis requires two thresholds – high and low. Select two thresholds TH and TL such that 0 6 TL < TH 6 255 Create two new binary 2-D arrays • gNH(x; y) = gN(x y) > TH • gNL(x; y) = gN(x y) > TL Eliminate from gNL(x; y) all the nonzero pixels from gNH(x y) After this operation • gNH(x y) will contain only the strong edge points • gNL(x y) will contain only the weak edge points • They will not contain common points Ratio of TH to TL should be 2 : 1 or 3 : 1
  15. 15. Canny Edge Detector Connectivity Analysis After step #4, gNH(x y) will contain the strong edge pixels { thus are valid edge pixels. However, there will be discontinuity in the edges. Longer edges are formed using the following procedure: 1 Add all the edge pixels in gNH(x y) to a list L 2 Do until there are more edge pixels in L • Locate the next unvisited edge pixel, p • Mark as valid edge pixels all the weak pixels in gNL(x y) that are connected to p (8 neighbors) • Remove p from L 3 Set to zero all pixels in gNL(x y) that were not marked as valid edge pixels 4 Combine all nonzero pixels in gNH(x y) and gNL(x y ) to find the final edge pixels Analysis
  16. 16. Canny Edge Detector Visually all steps
  17. 17. ?
  18. 18. Thank you! Jakir Hossain. ID: CSE-24th Batch. Southeast University.