1. Course Website: http://www.comp.dit.ie/bmacnameehttp://www.comp.dit.ie/bmacnamee Digital Image Processing: Digital Imaging Fundamentals P.M.Dholakia Brian Mac Namee Brian.MacNamee@dit.ie

2. 2 of 42 Contents This lecture will cover: –The human visual system –Light and the electromagnetic spectrum –Image representation –Image sensing and acquisition –Sampling, quantisation and resolution

3. 3 of 42 Human Visual System The best vision model we have! Knowledge of how images form in the eye can help us with processing digital images We will take just a whirlwind tour of the human visual system

4. 4 of 42 Structure Of The Human Eye The lens focuses light from objects onto the retina The retina is covered with light receptors called cones (6-7 million) and rods (75-150 million) Cones are concentrated around the fovea and are very sensitive to colour Rods are more spread out and are sensitive to low levels of illumination Images taken from Gonzalez & Woods, Digital Image Processing (2002)

5. 5 of 42 Blind-Spot Experiment Draw an image similar to that below on a piece of paper (the dot and cross are about 6 inches apart) Close your right eye and focus on the cross with your left eye Hold the image about 20 inches away from your face and move it slowly towards you The dot should disappear!

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7. 7 of 42 Image Formation In The Eye Muscles within the eye can be used to change the shape of the lens allowing us focus on objects that are near or far away An image is focused onto the retina causing rods and cones to become excited which ultimately send signals to the brain

8. 8 of 42 Example Example : assume that a 10m high structure is observed from a distance of 50 m. what is the size of the retianl image ? Ans : Let the size of the retinal image be x. It is assumed that the distance between the lens and retina is 17 mm. Therefore,

9. 9 of 42 Human Visual system Elements of Visual Perception The important property of human visual system that are necessary for the study of image processing are as follows : (1) Brightness Adaption General brightness adaption Local brightness Adaption Lateral Brightness Adaption (2) Intensity and Brightness (3) Simultaneous Contrast (4) Mach Bands (5) Frequency Response

10. 10 of 42 Brightness Adaption (1) Brightness Adaption : 2 distinct visual systems created by rods and cones Rods –not perceive color, sensitive 2 light Vision created by rods  SCOTOPIC vision Cones – sensitive to color, less sensitive 2 light Vision created by cones  PHOTOPIC vision Existence of 2 systems leads 2 large dynamic range 2 which human visual system can adapt. It cant operate over this entire range –simultaneously It changes it sensitivity to adapt dynamic range phenomenon called Brightness adaption.

11. 11 of 42 Brightness Adaption Plot of light intensity versus subjective brightness

12. 12 of 42 Brightness Adaption The human visual system can perceive approximately 10 10 different light intensity levels, from the Scotopic threshold to glare limit. Subjective brightness is a logarithmic function of the light intensity incident on the eye. (fig) Long solid curve  representation of the range of intensity which visual system can adapt. Photopic vision  range is about 10 6. Transition from Scotopic to Photopic  range from 0.001 to 0.1 millilambert ( -3 to -1 mL in log scale)

13. 13 of 42 Brightness Discrimination

14. 14 of 42 Brightness Discrimination If Δ I not bright enough subject says “NO” Indicating no perceivable changes. Δ I stronger subject may say “YES”  perceived changes Δ I strong enough subject will give “YES” all time The quantity Δ Ic/I, Δ Ic is the increment of illumination discriminable 50% of the time with background illumination I is called WEBER RATIO

15. 15 of 42 Brightness Discrimination A small value of Δ Ic/I  small percentage change in intensity is discriminable Good brightness discrimination A large value of Δ Ic/I  large percentage change in intensity is discriminable Poor brightness discrimination A plot of Δ Ic/I as a function of log I has the general shape shown in fig Curves shows brightness discrimination is poor ( weber ratio is large) at low level of illumination. It improves as background illumination increases. Low level carried out by Rods and high level is a function of Cones

16. 16 of 42 Brightness Discrimination Typical Weber ratio as a function of intensity

17. 17 of 42 Brightness Adaption General brightness adaption : Average brightness of the scene Ex : enter in dark room we cant see anything initially Increase in sensitivity of eye see things in darkness Brightness also influenced by contrast of light. Local brightness Adaption : When we view a scene, we view objects 1 by 1 Eye adjusts brightness adaption b4 moving 2 next object Transition is rapid—we often fail to notice of it. If Transition not smooth – leads to ‘after image’ effect Effect is felt when we stare at a bright object for a long period, and then suddenly see a darker object(image)

18. 18 of 42 Intensity and Brightness Lateral Brightness Adaption : Sensitivity changes in the local areas of the object are associated with similar changes in background Leads to the concept of simultaneous contrast. (2) Intensity and Brightness Light is physical phenomenon Measured and dependent on light surface and the angle from which it is emitted. Brightness is psycho-visual concept Perception or sensation of input light on the brain that produces concept of brightness. Contrast is the difference in the perceived brightness.

19. 19 of 42 Intensity and Brightness The change of intensity by some amount –does not result in equal change in perception. Brightness depends on luminance & surroundings Ex: white object on black background perceived better compare to brown background. Luminance of the object is same but perception of object is different. The resulting phenomenon known as Simultaneous Contrast and Mach Bands.

20. 20 of 42 (3) Simultaneous Contrast Human vision is influenced by the background Same intensity and colored squares perceived different in different backgrounds Appear bright in light background and vice versa (4) Mach Bands Mach Band effect is phenomenon of lateral inhibition of rods and cones Sharp intensity changes attenuated by the visual system There is a dip in brightness before transition & a peak following the transition. Transition have greater amplitude than normal & enhances the edges. Used to estimate impulse response of the visual system & measure response in spatial coordinates.

21. 21 of 42 Brightness Adaptation & Discrimination (cont…) An example of simultaneous contrast Images taken from Gonzalez & Woods, Digital Image Processing (2002)

22. 22 of 42 Brightness Adaptation & Discrimination (cont…) An example of Mach bands Images taken from Gonzalez & Woods, Digital Image Processing (2002)

23. 23 of 42 Brightness Adaptation & Discrimination (cont…) Images taken from Gonzalez & Woods, Digital Image Processing (2002)

24. 24 of 42 Brightness Adaptation & Discrimination (cont…) For more great illusion examples take a look at: http://web.mit.edu/persci/gaz/http://web.mit.edu/persci/gaz/

25. 25 of 42 Available here: http://www.lottolab.org/Visual%20Demos/Demo%2015.htmlhttp://www.lottolab.org/Visual%20Demos/Demo%2015.html

26. 26 of 42 Brightness Adaptation & Discrimination The human visual system can perceive approximately 10 10 different light intensity levels However, at any one time we can only discriminate between a much smaller number – brightness adaptation Similarly, the perceived intensity of a region is related to the light intensities of the regions surrounding it

27. 27 of 42 Optical Illusions Our visual systems play lots of interesting tricks on us

28. 28 of 42 Optical Illusions (cont…)

29. 29 of 42 Optical Illusions (cont…) Stare at the cross in the middle of the image and think circles

30. 30 of 42 Mind Map Exercise: Mind Mapping For Note Taking Beau Lotto: Optical Illusions Show How We See http://www.ted.com/talks/lang/eng/beau_lotto_optical_illusions_show_how_we_see.html

31. 31 of 42 Light And The Electromagnetic Spectrum Light is just a particular part of the electromagnetic spectrum that can be sensed by the human eye The electromagnetic spectrum is split up according to the wavelengths of different forms of energy

32. 32 of 42 Reflected Light The colours that we perceive are determined by the nature of the light reflected from an object For example, if white light is shone onto a green object most wavelengths are absorbed, while green light is reflected from the object White Light Colours Absorbed Green Light

33. 33 of 42 Sampling, Quantisation And Resolution In the following slides we will consider what is involved in capturing a digital image of a real-world scene –Image sensing and representation –Sampling and quantisation –Resolution

34. 34 of 42 Image Representation col row f (row, col) Before we discuss image acquisition recall that a digital image is composed of M rows and N columns of pixels each storing a value Pixel values are most often grey levels in the range 0-255(black-white) We will see later on that images can easily be represented as matrices Images taken from Gonzalez & Woods, Digital Image Processing (2002)

35. 35 of 42 Image Acquisition Images are typically generated by illuminating a scene and absorbing the energy reflected by the objects in that scene –Typical notions of illumination and scene can be way off: X-rays of a skeleton Ultrasound of an unborn baby Electro-microscopic images of molecules Images taken from Gonzalez & Woods, Digital Image Processing (2002)

36. 36 of 42 Image Sensing Incoming energy lands on a sensor material responsive to that type of energy and this generates a voltage Collections of sensors are arranged to capture images Imaging Sensor Line of Image Sensors Array of Image Sensors Images taken from Gonzalez & Woods, Digital Image Processing (2002)

37. 37 of 42 Image Sensing Images taken from Gonzalez & Woods, Digital Image Processing (2002) Using Sensor Strips and Rings

38. 38 of 42 Image Sampling And Quantisation A digital sensor can only measure a limited number of samples at a discrete set of energy levels Quantisation is the process of converting a continuous analogue signal into a digital representation of this signal Images taken from Gonzalez & Woods, Digital Image Processing (2002)

39. 39 of 42 Image Sampling And Quantisation Images taken from Gonzalez & Woods, Digital Image Processing (2002)

40. 40 of 42 Image Sampling And Quantisation Images taken from Gonzalez & Woods, Digital Image Processing (2002)

41. 41 of 42 Image Sampling And Quantisation (cont…) Remember that a digital image is always only an approximation of a real world scene Images taken from Gonzalez & Woods, Digital Image Processing (2002)

42. 42 of 42 Image Representation Images taken from Gonzalez & Woods, Digital Image Processing (2002)

43. 43 of 42 Image Representation Images taken from Gonzalez & Woods, Digital Image Processing (2002)

44. 44 of 42 Image Representation Images taken from Gonzalez & Woods, Digital Image Processing (2002)

45. 45 of 42 Image Representation Images taken from Gonzalez & Woods, Digital Image Processing (2002)

46. 46 of 42 Spatial Resolution The spatial resolution of an image is determined by how sampling was carried out Spatial resolution simply refers to the smallest discernable detail in an image –Vision specialists will often talk about pixel size –Graphic designers will talk about dots per inch (DPI) 5.1 Megapixels

47. 47 of 42 Spatial Resolution (cont…) Images taken from Gonzalez & Woods, Digital Image Processing (2002)

48. 48 of 42 Spatial Resolution (cont…) Images taken from Gonzalez & Woods, Digital Image Processing (2002)

49. 49 of 42 Spatial Resolution (cont…) Images taken from Gonzalez & Woods, Digital Image Processing (2002)

50. 50 of 42 Spatial Resolution (cont…) Images taken from Gonzalez & Woods, Digital Image Processing (2002)

51. 51 of 42 Spatial Resolution (cont…) Images taken from Gonzalez & Woods, Digital Image Processing (2002)

52. 52 of 42 Spatial Resolution (cont…) Images taken from Gonzalez & Woods, Digital Image Processing (2002)

53. 53 of 42 Spatial Resolution (cont…) Images taken from Gonzalez & Woods, Digital Image Processing (2002)

54. 54 of 42 Intensity Level Resolution Intensity level resolution refers to the number of intensity levels used to represent the image –The more intensity levels used, the finer the level of detail discernable in an image –Intensity level resolution is usually given in terms of the number of bits used to store each intensity level Number of Bits Number of Intensity Levels Examples 120, 1 2400, 01, 10, 11 4160000, 0101, 1111 825600110011, 01010101 1665,5361010101010101010

55. 55 of 42 Intensity Level Resolution (cont…) 128 grey levels (7 bpp) 64 grey levels (6 bpp)32 grey levels (5 bpp) 16 grey levels (4 bpp)8 grey levels (3 bpp) 4 grey levels (2 bpp)2 grey levels (1 bpp) 256 grey levels (8 bits per pixel) Images taken from Gonzalez & Woods, Digital Image Processing (2002)

56. 56 of 42 Intensity Level Resolution (cont…) Images taken from Gonzalez & Woods, Digital Image Processing (2002)

57. 57 of 42 Intensity Level Resolution (cont…) Images taken from Gonzalez & Woods, Digital Image Processing (2002)

58. 58 of 42 Intensity Level Resolution (cont…) Images taken from Gonzalez & Woods, Digital Image Processing (2002)

59. 59 of 42 Intensity Level Resolution (cont…) Images taken from Gonzalez & Woods, Digital Image Processing (2002)

60. 60 of 42 Intensity Level Resolution (cont…) Images taken from Gonzalez & Woods, Digital Image Processing (2002)

61. 61 of 42 Intensity Level Resolution (cont…) Images taken from Gonzalez & Woods, Digital Image Processing (2002)

62. 62 of 42 Intensity Level Resolution (cont…) Images taken from Gonzalez & Woods, Digital Image Processing (2002)

63. 63 of 42 Intensity Level Resolution (cont…) Images taken from Gonzalez & Woods, Digital Image Processing (2002)

64. 64 of 42 Saturation & Noise Images taken from Gonzalez & Woods, Digital Image Processing (2002)

65. 65 of 42 Resolution: How Much Is Enough? The big question with resolution is always how much is enough? –This all depends on what is in the image and what you would like to do with it –Key questions include Does the image look aesthetically pleasing? Can you see what you need to see within the image?

66. 66 of 42 Resolution: How Much Is Enough? (cont…) The picture on the right is fine for counting the number of cars, but not for reading the number plate

67. 67 of 42 Intensity Level Resolution (cont…) Images taken from Gonzalez & Woods, Digital Image Processing (2002) Low DetailMedium DetailHigh Detail

68. 68 of 42 Intensity Level Resolution (cont…) Images taken from Gonzalez & Woods, Digital Image Processing (2002)

69. 69 of 42 Intensity Level Resolution (cont…) Images taken from Gonzalez & Woods, Digital Image Processing (2002)

70. 70 of 42 Intensity Level Resolution (cont…) Images taken from Gonzalez & Woods, Digital Image Processing (2002)

71. 71 of 42 Summary We have looked at: –Human visual system –Light and the electromagnetic spectrum –Image representation –Image sensing and acquisition –Sampling, quantisation and resolution Next time we start to look at techniques for image enhancement