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Operations in Digital Image Processing + Convolution by Example

Digital image processing operations can be either point or group.
This presentation explains both operations (point and group) and shows how convolution works by a numerical example.

Ahmed Fawzy Gad
ahmed.fawzy@ci.menofia.edu.eg
Information Technology Department
Faculty of Computers and Information (FCI)
Menoufia University
Egypt

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Operations in Digital Image Processing + Convolution by Example

  1. 1. Operations in Digital Image Processing + Convolution by Example MENOUFIA UNIVERSITY FACULTY OF COMPUTERS AND INFORMATION ALL DEPARTMENTS ‫المنوفية‬ ‫جامعة‬ ‫والمعلومات‬ ‫الحاسبات‬ ‫كلية‬ ‫جميع‬‫األقسام‬ ‫المنوفية‬ ‫جامعة‬ Ahmed Fawzy Gad ahmed.fawzy@ci.menofia.edu.eg
  2. 2. Image Processing Operations Operations Point Group
  3. 3. Point Operations 58 3 213 81 78 185 87 32 27 11 70 66 60 2 19 61 91 129 89 38 14 7 58 14 42 Arithmetic Operations + - * / Add 2
  4. 4. Point Operations 58 3 213 81 78 185 87 32 27 11 70 66 60 2 19 61 91 129 89 38 14 7 58 14 42 Arithmetic Operations + - * / Add 2
  5. 5. Point Operations 60 3 213 81 78 185 87 32 27 11 70 66 60 2 19 61 91 129 89 38 14 7 58 14 42 Arithmetic Operations + - * / Add 2
  6. 6. Point Operations 60 3 213 81 78 185 87 32 27 11 70 66 60 2 19 61 91 129 89 38 14 7 58 14 42 Arithmetic Operations + - * / Add 2
  7. 7. Point Operations 60 5 213 81 78 185 87 32 27 11 70 66 60 2 19 61 91 129 89 38 14 7 58 14 42 Arithmetic Operations + - * / Add 2
  8. 8. Point Operations 60 5 213 81 78 185 87 32 27 11 70 66 60 2 19 61 91 129 89 38 14 7 58 14 42 Arithmetic Operations + - * / Add 2
  9. 9. Point Operations 60 5 215 81 78 185 87 32 27 11 70 66 60 2 19 61 91 129 89 38 14 7 58 14 42 Arithmetic Operations + - * / Add 2
  10. 10. Point Operations 60 5 215 83 80 187 89 34 29 13 72 68 62 4 21 63 93 131 91 40 16 9 60 16 44 Arithmetic Operations + - * / Add 2
  11. 11. Group Operations 58 3 213 81 78 185 87 32 27 11 70 66 60 2 19 61 91 129 89 38 14 7 58 14 42Mode Median Mean
  12. 12. Group Operations 58 3 213 81 78 185 87 32 27 11 70 66 60 2 19 61 91 129 89 38 14 7 58 14 42Mode Median Mean
  13. 13. Group Operations 58 3 213 81 78 185 87 32 27 11 70 66 60 2 19 61 91 129 89 38 14 7 58 14 42Mode Median Mean 58 + 3 + 213 + 185 + 87 + 32 + 70 + 66 + 60 9 = 𝟖𝟔
  14. 14. Group Operations – Template Operations 1 Operation 2 Template Mode Median Mean 7x7 5x5 3x3 ODD Why odd?
  15. 15. Group Operations 58 3 213 81 78 185 87 32 27 11 70 66 60 2 19 61 91 129 89 38 14 7 58 14 42Mode Median Mean 58 + 3 + 213 + 185 + 87 + 32 + 70 + 66 + 60 9 = 𝟖𝟔 Put the results in the center.
  16. 16. Group Operations 58 3 213 81 78 185 87 32 27 11 70 66 60 2 19 61 91 129 89 38 14 7 58 14 42Mode Median Mean 58 + 3 + 213 + 185 + 87 + 32 + 70 + 66 + 60 9 = 𝟖𝟔 Put the results in the center. 𝟖𝟔
  17. 17. Group Operations – Even Template Size 58 3 213 81 78 185 87 32 27 11 70 66 60 2 19 61 91 129 89 38 14 7 58 14 42Mode Median Mean 58 + 3 + 185 + 87 4 = 𝟖𝟑. 𝟐𝟓 ≅ 𝟖𝟒 Even template sizes has no center. E.g. 2x3, 2x2, 5x6, … 𝟖𝟒
  18. 18. Group Operations – Even Template Size 58 3 213 81 78 185 87 32 27 11 70 66 60 2 19 61 91 129 89 38 14 7 58 14 42Mode Median Mean Even template sizes has no center. E.g. 2x3, 2x2, 5x6, … 𝟖𝟒 ??? 58 + 3 + 185 + 87 4 = 𝟖𝟑. 𝟐𝟓 ≅ 𝟖𝟒
  19. 19. What is Convolution? • It is a mathematical way to combine two signals to generate a third signal. Convolution is not limited on digital image processing and it is a broad term that works on signals. Input Signal Impulse Response Output Signal System Convolution
  20. 20. What is Convolution? • It is a mathematical way to combine two signals to generate a third signal. Convolution is not limited on digital image processing and it is a broad term that works on signals. Input Signal Impulse Response Output Signal System Convolution 58 3 213 81 78 185 87 32 27 11 70 66 60 2 19 61 91 129 89 38 14 7 58 14 42
  21. 21. Convolution for 2D Image Signal 1 3 4 3 10 2 7 4 1 11 6 2 5 2 5 13 6 8 9 1 2 7 12 14 8Template – 3x3 2 1 -4 3 2 5 -1 8 1
  22. 22. Convolution for 2D Image Signal Template – 3x3 2 1 -4 3 2 5 -1 8 1 1 3 4 3 10 2 7 4 1 11 6 2 5 2 5 13 6 8 9 1 2 7 12 14 8
  23. 23. Convolution for 2D Image Signal Template – 3x3 2 1 -4 3 2 5 -1 8 1 1 3 4 3 10 2 7 4 1 11 6 2 5 2 5 13 6 8 9 1 2 7 12 14 8
  24. 24. Convolution for 2D Image Signal 1 3 4 3 10 2 44 4 1 11 6 2 5 2 5 13 6 8 9 1 2 7 12 14 8 2 ∗ 1 + 1 ∗ 3 − 4 ∗ 4 + 3 ∗ 2 + 2 ∗ 7 + 5 ∗ 4 − 1 ∗ 6 + 8 ∗ 2 + 1 ∗ 5 = 𝟒𝟒
  25. 25. 1 3 4 3 10 2 44 4 1 11 6 2 5 2 5 13 6 8 9 1 2 7 12 14 8 Convolution for 2D Image Signal 2 1 -4 3 2 5 -1 8 1
  26. 26. Convolution for 2D Image Signal 2 ∗ 3 + 1 ∗ 4 − 4 ∗ 3 + 3 ∗ 7 + 2 ∗ 4 + 5 ∗ 1 − 1 ∗ 2 + 8 ∗ 5 + 1 ∗ 2 = 𝟕𝟐 1 3 4 3 10 2 44 72 1 11 6 2 5 2 5 13 6 8 9 1 2 7 12 14 8
  27. 27. 1 3 4 3 10 2 44 72 1 11 6 2 5 2 5 13 6 8 9 1 2 7 12 14 8 Convolution for 2D Image Signal 2 1 -4 3 2 5 -1 8 1
  28. 28. 1 3 4 3 10 2 44 72 2 11 6 2 5 2 5 13 6 8 9 1 2 7 12 14 8 Convolution for 2D Image Signal 2 ∗ 4 + 1 ∗ 3 − 4 ∗ 10 + 3 ∗ 4 + 2 ∗ 1 + 5 ∗ 11 − 1 ∗ 5 + 8 ∗ 2 + 1 ∗ 5 = 𝟐
  29. 29. 1 3 4 3 10 2 44 72 2 11 6 2 5 2 5 13 6 8 9 1 2 7 12 14 8 Convolution for 2D Image Signal 2 1 -4 3 2 5 -1 8 1
  30. 30. 1 3 4 3 10 2 44 72 2 11 6 2 5 2 5 13 6 8 9 1 2 7 12 14 8 Convolution for 2D Image Signal 2 1 -4 3 2 5 -1 8 1
  31. 31. 1 3 4 3 10 2 44 72 2 82 6 2 5 2 5 13 6 8 9 1 2 7 12 14 8 Convolution for 2D Image Signal 2 ∗ 3 + 1 ∗ 10 − 4 ∗ 0 + 3 ∗ 2 + 2 ∗ 11 + 5 ∗ 0 − 1 ∗ 2 + 8 ∗ 5 + 1 ∗ 0 = 𝟖𝟐
  32. 32. 1 3 4 3 10 2 44 72 2 82 6 2 5 2 5 13 6 8 9 1 2 7 12 14 8 Convolution for 2D Image Signal
  33. 33. 1 3 4 3 10 2 44 72 2 82 6 2 5 2 5 13 6 8 9 1 2 7 12 14 8 Convolution for 2D Image Signal 2 1 -4 3 2 5 -1 8 1
  34. 34. 1 3 4 3 10 2 44 72 2 82 6 2 5 2 5 13 6 8 9 1 2 80 12 14 8 Convolution for 2D Image Signal 2 ∗ 13 + 1 ∗ 6 − 4 ∗ 8 + 3 ∗ 2 + 2 ∗ 7 + 5 ∗ 12 − 1 ∗ 0 + 8 ∗ 0 + 1 ∗ 0 = 𝟖𝟎
  35. 35. 1 3 4 3 10 2 44 72 2 82 6 2 5 2 5 13 6 8 9 1 2 80 12 14 8 Convolution for 2D Image Signal 2 1 -4 3 2 5 -1 8 1
  36. 36. 1 3 4 3 10 2 44 72 2 82 6 2 5 2 5 13 6 8 9 1 2 80 12 14 77 Convolution for 2D Image Signal 2 ∗ 9 + 1 ∗ 1 − 4 ∗ 0 + 3 ∗ 14 + 2 ∗ 8 + 5 ∗ 0 − 1 ∗ 0 + 8 ∗ 0 + 1 ∗ 0 = 𝟕𝟕
  37. 37. Convolution for 2D Signal •Continue. 1 3 4 3 10 2 44 72 2 82 6 2 5 2 5 13 6 8 9 1 2 80 12 14 77
  38. 38. Convolution for 2D Signal •Template for Mean.𝟏 𝟗 𝟏 𝟗 𝟏 𝟗 𝟏 𝟗 𝟏 𝟗 𝟏 𝟗 𝟏 𝟗 𝟏 𝟗 𝟏 𝟗 58 3 213 81 78 185 87 32 27 11 70 66 60 2 19 61 91 129 89 38 14 7 58 14 42 𝟏 𝟗 ∗ 58 + 𝟏 𝟗 ∗ 3 + 𝟏 𝟗 ∗ 213 + 𝟏 𝟗 ∗ 185 + 𝟏 𝟗 ∗ 87 + 𝟏 𝟗 ∗ 32 + 𝟏 𝟗 ∗ 70 + 𝟏 𝟗 ∗ 66 + 𝟏 𝟗 ∗ 60 = 86

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