The document serves as an introductory guide to digital image processing, explaining its relevance in various fields such as photography, robotics, and medical imaging. It covers essential concepts such as pixel representation, image loading, filtering techniques, edge detection, and segmentation. Additionally, it lists useful tools and provides examples of image processing techniques using software like MATLAB and Python.

1. Introduction toIntroduction to
Digital ImageDigital Image
ProcessingProcessing

2. Digital Image ProcessingDigital Image Processing
• Digital image processing is the use of computer
algorithms to perform image processing on
digital images.
Taken from Wikipedia

3. Why should we care?Why should we care?
• Everyone has a digital camera
• Everyone shares photos and videos
• Concepts can be used in videogames
• Robotics
• New technologies (Google Glass, Oculus Rift)
• Useful in other fields of science (medical
imaging, astronomy)
• Many frameworks for image processing
• It’s just fun

4. What is a digital image?What is a digital image?

5. A Pixel!!!!A Pixel!!!!

6. Pixel!!!!Pixel!!!!

7. IR y UVIR y UV

8. Representing PixelsRepresenting Pixels

9. Representing Pixels: 2Representing Pixels: 2

10. Representing Pixels: 4Representing Pixels: 4

11. Representing Pixels: 8Representing Pixels: 8

12. Representing Pixels: 16Representing Pixels: 16

13. Representing Pixels: 32Representing Pixels: 32

14. Representing Pixels: 64Representing Pixels: 64

15. Representing Pixels: 128Representing Pixels: 128

16. Representing Pixels: 256Representing Pixels: 256

17. Colors: RGB spaceColors: RGB space

18. Colors: RGBColors: RGB

19. Lab: Image loading, colorLab: Image loading, color
channelschannels
img = imread ( 'Desktop/guitarra.jpg' );
img_size = size ( img )
img_red = uint8 ( zeros ( img_size ) );
img_green = uint8 ( zeros (img_size) );
img_blue = uint8 ( zeros ( img_size ) );

20. Lab: Image loading, colorLab: Image loading, color
channelschannels
img_red (:, :, 1 ) = img ( :, :, 1 );
img_green (:, :, 2 ) = img ( :, :, 2 );
img_blue (:, :, 3 ) = img ( :, :, 3 );
imshow ( img );
imshow ( img_red );
imshow ( img_green );
imshow ( img_blue );

21. Lab: Image loading, colorLab: Image loading, color
channelschannels
img = rgb2gray ( img );
imshow ( img );

22. Image: Matrix of Pixel ValuesImage: Matrix of Pixel Values

23. Image: Matrix of Pixel ValuesImage: Matrix of Pixel Values

24. Imagen: Matriz de PixelesImagen: Matriz de Pixeles

25. Imagen: Matriz de PixelesImagen: Matriz de Pixeles

26. Imagen: Matriz de PixelesImagen: Matriz de Pixeles

27. Image: Matrix of Pixel ValuesImage: Matrix of Pixel Values

28. Image: Matrix of Pixel ValuesImage: Matrix of Pixel Values

29. Image: Matrix of Pixel ValuesImage: Matrix of Pixel Values

30. Image: Matrix of Pixel ValuesImage: Matrix of Pixel Values

31. Lab: Gamma CorrectLab: Gamma Correct
function output = gcorrect ( i, g )
i = double ( i );
output = ( i / 255).^g * 255;
output = uint8 ( output );
end;
imshow ( gcorrect (img, 2 ) );
imshow ( gcorrect (img, 0.5 ) );

32. Guess the PixelGuess the Pixel

33. Image: Matrix of Pixel ValuesImage: Matrix of Pixel Values

34. Imagen: Matriz de PixelesImagen: Matriz de Pixeles

35. Image: Matrix of Pixel ValuesImage: Matrix of Pixel Values

36. Image: Pixel NeighborhoodImage: Pixel Neighborhood
• Every pixel has neighbors

37. Image: Pixel NeighborhoodImage: Pixel Neighborhood

38. Image: Pixel NeighborhoodImage: Pixel Neighborhood

39. Image: Pixel NeighborhoodImage: Pixel Neighborhood
5 8 7
4 1 6
3 2 9
1 2 3 4 5 6 7 8 95

40. Lab: Median FilterLab: Median Filter
img_noise = imnoise (img, 'salt & pepper', 0.4);
imshow ( img_noise );
img_filt = medfilt2 ( img_noise );
imshow ( img_filt );

41. Image: Pixel NeighborhoodImage: Pixel Neighborhood

42. Image: Pixel NeighborhoodImage: Pixel Neighborhood
5x5 9x9 13x13 17x17 21x21 25x25 29x29

43. Image: Pixel NeighborhoodImage: Pixel Neighborhood

44. Image: Pixel NeighborhoodImage: Pixel Neighborhood

45. Image: Pixel NeighborhoodImage: Pixel Neighborhood

46. Image: f(x,y)Image: f(x,y)

47. Imagen: f(x,y)Imagen: f(x,y)

48. Image: f(x,y)Image: f(x,y)

49. Image: f(x,y)Image: f(x,y)

50. Image: f(x,y)Image: f(x,y)

51. Image: f(x,y)Image: f(x,y)

52. Image: f(x,y)Image: f(x,y)
Laplacian Derivative

53. Imagen: f(x,y)Imagen: f(x,y)

54. Image: f(x,y)Image: f(x,y)

55. Image: f(x,y)Image: f(x,y)

56. Image: f(x,y), Gradients, SobelImage: f(x,y), Gradients, Sobel

57. Image: f(x,y), Gradients, SobelImage: f(x,y), Gradients, Sobel

58. Image: f(x,y), Gradients, SobelImage: f(x,y), Gradients, Sobel

59. Image: f(x,y), Gradients, SobelImage: f(x,y), Gradients, Sobel

60. Image: f(x,y), Gradients, SobelImage: f(x,y), Gradients, Sobel

61. Image: f(x,y), Gradients, SobelImage: f(x,y), Gradients, Sobel

62. Lab: Edge DetectionLab: Edge Detection
Step 1: Create a Gauss filter
gauss_fil = [
2 4 5 4 2
4 9 12 9 4
5 12 15 12 5
4 9 12 9 4
2 4 5 4 2 ]
sum ( sum (gauss_fil ) )
gauss_fil = gauss_fil / 159

63. Lab: Edge DetectionLab: Edge Detection
Step 2: Apply Gauss filter
img_blur = conv2 ( double ( img ), gauss_fil );
img_blur = uint8 ( img_blur );
figure, imshow ( img ), figure, imshow ( img_blur );

64. Lab: Edge DetectionLab: Edge Detection
Step 3: Create kernels to calculate gradient
ker_gx = [
-1 0 1
-2 0 2
-1 0 1 ]
ker_gy = [
1 2 1
0 0 0
-1 -2 -1]

65. Lab: Edge DetectionLab: Edge Detection
Step 4: Calculate gradient
gx = conv2 ( double ( img ), ker_gx );
gy = conv2 ( double ( img ), ker_gy );
img_g = abs( gx ) + abs( gy );
img_g = uint8 ( img_g );
imshow ( img_g );

66. Lab: Edge DetectionLab: Edge Detection
Step 5: Suppression of non-maxima

67. Lab: Edge DetectionLab: Edge Detection
Step 6: Two thresholds
Step 7: Hysteresis
img_c = edge (img, 'canny' );
figure, imshow(img_g), figure, imshow ( img_c );

68. Image: f(x,y), Hough TransformImage: f(x,y), Hough Transform

69. Image: f(x,y), Hough TransformImage: f(x,y), Hough Transform
0 45 90 135
1
2
3
4
5
6
7

70. Imagen: f(x,y), Transformada deImagen: f(x,y), Transformada de
HoughHough

71. Image: f(x,y), Hough TransformImage: f(x,y), Hough Transform

72. Lab: Segment DetectionLab: Segment Detection
• Visit: www.ipol.im
• Segmentation and Edges
• LDS: a Line Segment Detector

73. Lab: SegmentationLab: Segmentation
• Visit: www.ipol.im
• Segmentation and Edges
• Chan-Vese Segmentation
• Consider: A Real Time Morphological Snakes
Algorithm

74. Things to ConsiderThings to Consider
• Quadtrees
• Review Graph Theory (Laplace, Gradiantes,
Watersheds)
– Strongly Connected Components
– Minimum Cut Algorithm
• Gather feedback from the user (make the user
select areas or faces)
• Sparse Modeling
• Machine Learning

75. WatershedsWatersheds

76. Solving a Maze with ImageSolving a Maze with Image
Processing?Processing?

77. Solving a Maze with ImageSolving a Maze with Image
Processing?Processing?

78. Solving a Maze with ImageSolving a Maze with Image
Processing?Processing?

79. Usefool ToolsUsefool Tools
• Matlab / Octave
• Mathematica
• OpenCV
• ipol.im
• Python
• Photoshop

80. Thanks for watching.Thanks for watching.
• Email: jseaman@squadventure.com
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