pengantar pengolahan citra digital tahap 1

pengantar pengolahan citra digital tahap 1

1. Chapter-1 dan 2 Pengolahan Citra Dijital

2. Digital Image Processing, 3 rd edition by Gonzalez and Woods

3. Optics and Human Vision The physics of light http://commons.wikimedia.org/wiki/File:Eye-diagram_bg.svg

4. Light Light Particles known as photons Act as ‘waves’ Two fundamental properties Amplitude Wavelength Frequency is the inverse of wavelength Relationship between wavelength (lambda) and frequency (f) Where c = speed of light = 299,792,458 m / s 4

5. What is Digital Image Processing? Digital image processing focuses on two major tasks Improvement of pictorial information for human interpretation Processing of image data for storage, transmission and representation for autonomous machine perception Some argument about where image processing ends and fields such as image analysis and computer vision start

6. What is DIP? (cont…) The continuum from image processing to computer vision can be broken up into low-, mid- and high-level processes Low Level Process Input: Image Output: Image Examples: Noise removal, image sharpening Mid Level Process Input: Image Output: Attributes Examples: Object recognition, segmentation High Level Process Input: Attributes Output: Understanding Examples: Scene understanding, autonomous navigation In this course we will stop here

7. History of Digital Image Processing Early 1920s: One of the first applications of digital imaging was in the news- paper industry The Bartlane cable picture transmission service Images were transferred by submarine cable between London and New York Pictures were coded for cable transfer and reconstructed at the receiving end on a telegraph printer Early digital image Images taken from Gonzalez & Woods, Digital Image Processing (2002)

8. History of DIP (cont…) Mid to late 1920s: Improvements to the Bartlane system resulted in higher quality images New reproduction processes based on photographic techniques Increased number of tones in reproduced images Improved digital image Early 15 tone digital image Images taken from Gonzalez & Woods, Digital Image Processing (2002)

9. History of DIP (cont…) 1960s: Improvements in computing technology and the onset of the space race led to a surge of work in digital image processing 1964: Computers used to improve the quality of images of the moon taken by the Ranger 7 probe Such techniques were used in other space missions including the Apollo landings A picture of the moon taken by the Ranger 7 probe minutes before landing Images taken from Gonzalez & Woods, Digital Image Processing (2002)

10. History of DIP (cont…) 1970s: Digital image processing begins to be used in medical applications 1979: Sir Godfrey N. Hounsfield & Prof. Allan M. Cormack share the Nobel Prize in medicine for the invention of tomography, the technology behind Computerised Axial Tomography (CAT) scans Typical head slice CAT image Images taken from Gonzalez & Woods, Digital Image Processing (2002)

11. Key Stages in Digital Image Processing Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression

12. Key Stages in Digital Image Processing: Image Aquisition Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)

13. Key Stages in Digital Image Processing: Image Enhancement Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)

14. Key Stages in Digital Image Processing: Image Restoration Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)

15. Key Stages in Digital Image Processing: Morphological Processing Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)

16. Key Stages in Digital Image Processing: Segmentation Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)

17. Key Stages in Digital Image Processing: Object Recognition Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)

18. Key Stages in Digital Image Processing: Representation & Description Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)

19. Key Stages in Digital Image Processing: Image Compression Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression

20. Key Stages in Digital Image Processing: Colour Image Processing Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression

21. Visible Spectrum (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2 nd Edition.

22. Light Diagram of a light wave. 22

23. Conventional Coordinate for Image Representation (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2 nd Edition.

24. Digital Image Types : Intensity Image Intensity image or monochrome image each pixel corresponds to light intensity normally represented in gray scale (gray level). Gray scale values

25. Digital Image Types : RGB Image Color image or RGB image : each pixel contains a vector representing red, green and blue components. RGB components

26. Image Types : Binary Image Binary image or black and white image Each pixel contains one bit : 1 represent white 0 represents black Binary data

27. Image Types : Index Image Index image Each pixel contains index number pointing to a color in a color table Index value Index No. Red component Green component Blue component 1 0.1 0.5 0.3 2 1.0 0.0 0.0 3 0.0 1.0 0.0 4 0.5 0.5 0.5 5 0.2 0.8 0.9 … … … … Color Table

28. Cross Section of the Human Eye (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2 nd Edition.

29. Human Eye 29

30. Anatomy of the Human Eye 30 Source: http://webvision.med.utah.edu/

31. Human Visual System Human vision Cornea acts as a protective lens that roughly focuses incoming light Iris controls the amount of light that enters the eye The lens sharply focuses incoming light onto the retina Absorbs both infra-red and ultra-violet light which can damage the lens The retina is covered by photoreceptors (light sensors) which measure light 31

32. Photoreceptors Rods Approximately 100-150 million rods Non-uniform distribution across the retina Sensitive to low-light levels ( scotopic vision) Lower resolution Cones Approximately 6-7 million cones Sensitive to higher-light levels ( photopic vision) High resolution Detect color by the use of 3 different kinds of cones each of which is sensitive to red, green, or blue frequencies Red (L cone) : 564-580 nm wavelengths (65% of all cones) Green (M cone) : 534-545 nm wavelengths (30% of all cones) Blue (S cone) : 420-440 nm wavelengths (5% of all cones) 33

33. Cone (LMS) and Rod (R) responses http://en.wikipedia.org/wiki/File:Cone-response.svg 34

34. Photoreceptor density across retina 35

35. Comparison between rods and cones 36 Rods Cones Used for night vision Used for day vision Loss causes night blindness Loss causes legal blindness Low spatial resolution with higher noise High spatial resolution with lower noise Not present in fovea Concentrated in fovea Slower time response to light Quicker time response to light One type of photosensitive pigment Three types of photosensitive pigment Emphasis on motion detection Emphasis on detecting fine detail

36. Color and Human Perception Chromatic light has a color component Achromatic light has no color component has only one property – intensity 37

37. Image Formation in the Human Eye (Picture from Microsoft Encarta 2000) (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2 nd Edition.

38. Brightness Adaptation Actual light intensity is (basically) log-compressed for perception. Human vision can see light between the glare limit and scotopic threshold but not all levels at the same time. The eye adjusts to an average value (the red dot) and can simultaneously see all light in a smaller range surrounding the adaptation level. Light appears black at the bottom of the instantaneous range and white at the top of that range. 39

39. Weber Ratio ∆I/I

40. Weber Ratio

41. Human Visual Perception Light intensity : The lowest (darkest) perceptible intensity is the scotopic threshold The highest (brightest) perceptible intensity is the glare limit The difference between these two levels is on the order of 10 10 We can’t discriminate all these intensities at the same time ! We adjust to an average value of light intensities and then discriminate around the average. Log compression. Experimental results show that the relationship between the perceived amount of light and the actual amount of light in a scene are generally related logarithmically. The human visual system perceives brightness as the logarithm of the actual light intensity and interprets the image accordingly. Consider, for example, a bright light source that is approximately 6times brighter than another. The eye will perceive the brighter light as approximately twice the brightness of the darker. 42

42. Brightness Adaptation and Mach Banding 43 When viewing any scene: The eye rapidly scans across the field of view while coming to momentary rest at each point of particular interest. At each of these points the eye adapts to the average brightness of the local region surrounding the point of interest. This phenomena is known as local brightness adaptation. Mach banding is a visual effect that results, in part, from local brightness adaptation. The eye over-shoots/under-shoots at edges where the brightness changes rapidly. This causes ‘false perception’ of the intensities Examples follow….

43. Brightness Adaptation and Mach Banding 44

44. Brightness Adaptation(Hermann Grid) 45

45. 46

46. Optical illusion (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2 nd Edition.

47. Simultaneous Contrast Simultaneous contrast refers to the way in which two adjacent intensities (or colors) affect each other. Example: Note that a blank sheet of paper may appear white when placed on a desktop but may appear black when used to shield the eyes against the sun. Figure 2.9 is a common way of illustrating that the perceived intensity of a region is dependent upon the contrast of the region with its local background. The four inner squares are of identical intensity but are contextualized by the four surrounding squares The perceived intensity of the inner squares varies from bright on the left to dark on the right. 48

48. Simultaneous Contrast 49

49. Image Sensing and acquisition Single sensor Line sensor Array sensor (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2 nd Edition.

50. Image Sensors : Single Sensor (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2 nd Edition.

51. Image Sensors : Line Sensor Fingerprint sweep sensor Computerized Axial Tomography (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2 nd Edition.

52. CCD KAF-3200E from Kodak. (2184 x 1472 pixels, Pixel size 6.8 microns 2 ) Charge-Coupled Device (CCD) w Used for convert a continuous image into a digital image w Contains an array of light sensors w Converts photon into electric charges accumulated in each sensor unit Image Sensors : Array Sensor

53. Horizontal Transportation Register Output Gate Amplifier Vertical Transport Register Gate Vertical Transport Register Gate Vertical Transport Register Gate Photosites Output Image Sensor: Inside Charge-Coupled Device

54. Image Sensor: How CCD works a b c g h i d e f a b c g h i d e f a b c g h i d e f Vertical shift Horizontal shift Image pixel Horizontal transport register Output