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Edge detection

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Edge detection

  1. 1. Edge Detection Presented By Ishraq Fatafta
  2. 2. AgendaO What is an edge.O What is edge detection O Usage of edge detection. O Type of edges. O Background.O Edge detection methods O Gradient based methods. O Zero Crossing based. O Proposed Algorithm.
  3. 3. EdgesO Abrupt change in the intensity of pixels.O Discontinuity in image brightness or contrast.O Usually edges occur on the boundary of two regions .
  4. 4. Edge DetectionO Process of identifying edges in an image to be used as a fundamental asset in image analysis.O Locating areas with strong intensity contrasts.
  5. 5. Edge Detection UsageO Reduce unnecessary information in the image while preserving the structure of the image.O Extract important features of an image O Corners O Lines O CurvesO Recognize objects, boundaries, segmentation.O Part of computer vision and recognition.
  6. 6. Edge TypesO Step EdgeO Ramp EdgeO RidgeO Roof
  7. 7. Edge Detection Background O Classical Gradient Edge detection. O Sobel, Prewitt, Kirsch and Robinson. O Gaussian based filters O Marr and Hildreth. O Canny O Shunck, Witkin and Bergholm. O Wavelets used for different scales. O Heric and Zazula and Shih and Tseng. O Fuzzy Logic and Neural Networks.
  8. 8. Edge Detection StepsO Smoothing: Noise Reduction.O Enhancement: Edge sharpening.O Detection: Which to discard and which to maintain. O Thresholding.O Localization: determine the exact location of an edge. O Edge thinning and linking are usually required in this step.
  9. 9. Methods of Edge DetectionO Gradient methods (First Order Derivative) O local maxima and minima using first derivative in an image. O Compute Gradient magnitude horizontally and vertically.O Zero-crossing methods (Second Order Derivative) O locate zeros in the second derivative of an image. O Laplacian of an Image.
  10. 10. Gradient based Edge DetectionO Best used for abrupt discontinuities.O Perform better in less noised imagesO Magnitude of the gradient - strength of the edge .O Direction - opposite of the edge direction. Gy G Gx 2 Gy 2 Gx Gy tan 1 Gx
  11. 11. Cont. Gradient based Edge DetectionO Roberts Edge Detector.O Prewitt Edge Detector.O Sobel Edge Detector,O Canny Edge Detector.
  12. 12. Cont. Gradient based Edge Detection - RobertsO 2X2 Convolution MaskO Convolution Mask O Gx Gy 1 0 0 -1 0 -1 1 0O Differences are computed at the interpolated points [i+1/2, j+1/2] and not [i, j].O Responds to edge with 450.
  13. 13. Cont. Gradient based Edge Detection - PrewittO 3X3 Convolution MaskO Convolution Mask O Gx -1 0 1 Gy 1 1 1 -1 0 1 0 0 0 -1 0 1 -1 -1 -1O The differences are calculated at the center pixel of the mask.
  14. 14. Cont. Gradient based Edge Detection - SobelO 3X3 Convolution MaskO Convolution Mask O Gx -1 0 1 Gy 1 2 1 -2 0 2 0 0 0 -1 0 1 -1 -2 -1O The differences are calculated at the center pixel of the mask.
  15. 15. Cont. Gradient based Edge DetectionO Simple to implementO Capable of detecting edges and their directionO Sensitive to noiseO Not accurate in locating edges
  16. 16. Cont. Gradient based Edge Detection - CannyO First derivative of a Gaussian filter will approximately optimize the signal-to-noise ratio and localization.
  17. 17. Cont. Gradient based Edge Detection - CannyO Three conditions for optimal detector O Error rate: Respond to edges not noise. O Localization: edges detected near true edges. O Response - Not identify multiple edge pixels.
  18. 18. Cont. Gradient based EdgeDetection – Canny AlgorithmO G Gx 2 Gy 2 Gx Gy
  19. 19. Cont. Gradient based EdgeDetection – Canny AlgorithmO Step 3 O Edge Direction 1 Gy tan GxO Step 4 O Resolve Edge Direction
  20. 20. Cont. Gradient based EdgeDetection – Canny AlgorithmO Step 5 O Non-maxima suppression: keep all local maxima in the gradient and remove everything else. O Gives a thin line for edgeO Step 6 O Double / hysteresis thresholding
  21. 21. Cont. Gradient based EdgeDetection – Canny AlgorithmO Better localizationO Improved signal-to-noise ratio.O Works fine under noisy conditions.O Complex to implement and time consuming.
  22. 22. Gradient based Edge
  23. 23. Zero Crossing based Edge DetectionO Indicates the presence of a maxima.O Pixel value passes through zero (changes its sign).
  24. 24. Cont. Zero Crossing based Edge DetectionO Laplacian of Gaussian 1 1 1 1 8 1 1 1 1 -1 2 -1 2 4 2 -1 2 -1
  25. 25. Cont. Zero Crossing based Edge Detection - LOGO Defined as:O Greater the value of , broader is the Gaussian filter, more is the smoothing
  26. 26. Cont. Zero Crossing based Edge Detection - LOGO Steps: O Smoothing: Gaussian filter O Enhance edges: Laplacian operator O Zero crossings denote the edge location O Use linear interpolation to determine the sub-pixel location of the edge
  27. 27. Cont. Zero Crossing based Edge Detection - LOGO Computationally cheaper to implement since we can combine the two filters into one filter but it.O Doesn’t provide information about the direction of the edge.O Probability of false and missing edges remain.O Localization is better than Gradient Operators
  28. 28. Laplacian Of Gaussain
  29. 29. Proposed AlgorithmO Evaluate an Image using a Gradient filter.O Evaluate an image using a Zero crossing filter.O Fuzzy Logic.
  30. 30. Cont. Proposed Algorithm Fuzzy LogicO Fuzzy Logic O Problem-solving methodology. O Draw definite conclusions from vague, ambiguous or imprecise information. O Resembles human decision making with its ability to work from approximate data and find precise solutions.
  31. 31. Cont. Proposed Algorithm Fuzzy Image ProcessingO Collection of all approaches that understand, represent and process the images, their segments and features as fuzzy sets.O The representation and processing depend on the selected fuzzy technique and on the problem to be solved.
  32. 32. Cont. Proposed Algorithm Fuzzy Image Processing
  33. 33. Cont. Proposed AlgorithmO Step 1 O Calculate gradient of an image using Sobel filter.O Step 2 O Calculate zero crossing using three different values of standard deviation σ in gausian of laplacian.O Step 3 O Apply fuzzy rules on the two steps above.
  34. 34. Cont. Proposed Algorithm Step 1 - SobelO The resulted Gradient are mapped from [0-255]O Divide into four regions: O Low class GL from [0-GL]. O Medium class GM O from [GL-GM] . O From [GM-GH] . O High class GH from [GH-255].
  35. 35. Cont. Proposed Algorithm Step 1 - Sobel
  36. 36. Cont. Proposed Algorithm Step 2 - LOGO
  37. 37. Cont. Proposed Algorithm Step 2 - LOGO Subtract three results.O Apply an algorithm.
  38. 38. Cont. Proposed Algorithm Step 3 – Fuzzy setsO Pixel (PG), zero crossing value (PZ), and probability of a pixel corresponds to an edge {EL, EM, EH}.O If PG is in GL and PZ equals to 1, then P belongs to EL.O If PG is in GL and PZ equals to a zero, then P belongs to EL.O If PG is in [GL-GM] and PZ equals to 1, then P belongs to EM.O If PG is in [GL-GM] and PZ equals to zero, then P belongs to EL.O If PG is in [GM-GH] and PZ equals to 1, then P belongs to EH.O If PG is in [GM-GH] and PZ equals to zero, then P belongs to EM.O If PG is in GH and PZ equals to 1, then P belongs to EH.O If PG is in GH and PZ equals to zero, then P belongs to EM.
  39. 39. Cont. Proposed Algorithm ResultO All pixels in the EH will be considered as an edge.O All pixels in the EL will be discarded.O All pixels in the EM, the gradient value will be evaluated against a threshold value in order to discard any pixel with value (0) that may result from false zero crossing.

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