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Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
Image processing Presentation
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Image processing Presentation

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Image processing Gongalez

Image processing Gongalez

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  • As opposed to [0..255]
  • Render with scanalyze????
  • Use photoshop to make something grayscale
  • Transcript

    • 1. Image Processing Ref: Digital image processing ,Gonzalez & Woods
    • 2. Components of an Image Processing System: Ref: Digital image processing ,Gonzalez & Woods
    • 3. Image sensing 1. a physical device that is sensitive to the energy radiated by the object. 2. a digitizer
      • Technologies used
      • 1. Photo chemical, eg: photographic film
      • 2. Photo electronic
      • The devices used
      • Still camera
      • TV camera
      • 3. X ray scanner
      • 4. Radar
      • 5. Magnetic resonance imaging (MRI) system.
    • 4. Digitizer: Ref: Digital image processing ,Gonzalez & Woods Commonly used digitizers 1. Microdensitometer and 2. flying spot scanner.
    • 5. Digital storage : Ref: Digital image processing ,Gonzalez & Woods categorized as (1) short-term storage for use during processing, eg: frame buffers. (2) on-line storage for relatively fast re-call, and (3) archival storage, characterized by infrequent access.
    • 6. Image displays: Ref: Digital image processing ,Gonzalez & Woods Principal display devices are 1. printers, 2.TV monitor, 3. CRTs Hardcopy: laser printers, film cameras, heat-sensitive devices, inkjet units, and digital units, such as optical and CD-ROM disks, paper etc.
    • 7. Structure of the Human Eye
    • 8. Ref: Digital image processing ,Gonzalez & Woods
    • 9. Ref: Digital image processing ,Gonzalez & Woods
    • 10. Ref: Digital image processing ,Gonzalez & Woods
    • 11. Ref: Digital image processing ,Gonzalez & Woods
    • 12. Ref: Digital image processing ,Gonzalez & Woods
    • 13. Ref: Digital image processing ,Gonzalez & Woods
    • 14. Ref: Digital image processing ,Gonzalez & Woods
    • 15. Ref: Digital image processing ,Gonzalez & Woods
    • 16. Ref: Digital image processing ,Gonzalez & Woods
    • 17. Ref: Digital image processing ,Gonzalez & Woods
    • 18. Ref: Digital image processing ,Gonzalez & Woods
    • 19. Image Formation
    • 20. Sampling and Quantization
    • 21. Sampling and Quantization
    • 22. Ref: Digital image processing ,Gonzalez & Woods
    • 23. Ref: Digital image processing ,Gonzalez & Woods Spatial and Gray-Level Resolution: Spatial Resolution: is the smallest discernible detail in an image. Gray—level resolution: refers to the smallest discernible change in gray level . -subjective process.
    • 24. Ref: Digital image processing ,Gonzalez & Woods
    • 25. Ref: Digital image processing ,Gonzalez & Woods
    • 26. Ref: Digital image processing ,Gonzalez & Woods
    • 27. Ref: Digital image processing ,Gonzalez & Woods
    • 28. Ref: Digital image processing ,Gonzalez & Woods
    • 29. Ref: Digital image processing ,Gonzalez & Woods
    • 30. Ref: Digital image processing ,Gonzalez & Woods Aliasing and Moiré Patterns:
    • 31. Ref: Digital image processing ,Gonzalez & Woods Zooming and Shrinking Digital Images:
      • Zooming
      • creation of new pixel locations
      • assignment of gray levels to those new locations.
      • 1.Nearest neighbor interpolation
      • 2. Pixel replication
      • 3. Bilinear interpolation
      • v(x', y') = ax' + by' + cx'y' + d
      • a= f (1, 0)- f (0, 0)
      • b= f (0, 1)- f (0, 0)
      • c= f (0, 0)- f (1, 0)- f (0, 1)+ f (1, 1)
      • d= f (0, 0)
      • Image shrinking
    • 32. Ref: Digital image processing ,Gonzalez & Woods
    • 33. Ref: Digital image processing ,Gonzalez & Woods   2D bilenear interpolation                                                                                                        Original   Before After No Interpolation
    • 34. Ref: Digital image processing ,Gonzalez & Woods
    • 35. Ref: Digital image processing ,Gonzalez & Woods
    • 36. Basic Point Processing
    • 37. Ref: Digital image processing ,Gonzalez & Woods
    • 38. Log
    • 39. Power-law transformations
    • 40. Gamma Correction Gamma Measuring Applet: http:// www.cs.berkeley.edu/~efros/java/gamma/gamma.html
    • 41. Ref: Digital image processing ,Gonzalez & Woods
    • 42. Image Enhancement
    • 43. Contrast Streching
    • 44. Ref: Digital image processing ,Gonzalez & Woods
    • 45. Ref: Digital image processing ,Gonzalez & Woods
    • 46. Ref: Digital image processing ,Gonzalez & Woods
    • 47. Ref: Digital image processing ,Gonzalez & Woods
    • 48. Histogram Equalization
    • 49. Ref: Digital image processing ,Gonzalez & Woods
    • 50. Ref: Digital image processing ,Gonzalez & Woods
    • 51. Ref: Digital image processing ,Gonzalez & Woods
    • 52. Ref: Digital image processing ,Gonzalez & Woods Periodic noise
    • 53. Ref: Digital image processing ,Gonzalez & Woods
    • 54. Mean and variance of the gray levels in image strips
    • 55. Ref: Digital image processing ,Gonzalez & Woods
    • 56. Ref: Digital image processing ,Gonzalez & Woods
    • 57. Ref: Digital image processing ,Gonzalez & Woods Wrong sign for Q
    • 58. Ref: Digital image processing ,Gonzalez & Woods
    • 59. Ref: Digital image processing ,Gonzalez & Woods Max and Min filter
    • 60. Ref: Digital image processing ,Gonzalez & Woods Alpha trimmed mean filter with d=5
    • 61. Ref: Digital image processing ,Gonzalez & Woods Adaptive noise reduction filtering
    • 62. Ref: Digital image processing ,Gonzalez & Woods
    • 63. What is an image?
      • We can think of an image as a function, f , from R 2 to R:
        • f ( x, y ) gives the intensity at position ( x, y )
        • Realistically, we expect the image only to be defined over a rectangle, with a finite range:
          • f : [ a , b ] x [ c , d ]  [0,1]
      • A color image is just three functions pasted together. We can write this as a “vector-valued” function:
    • 64. Images as functions
    • 65. What is a digital image?
      • We usually operate on digital ( discrete ) images:
        • Sample the 2D space on a regular grid
        • Quantize each sample (round to nearest integer)
      • If our samples are  apart, we can write this as:
      • f [ i , j ] = Quantize{ f ( i  , j  ) }
      • The image can now be represented as a matrix of integer values
    • 66. Image processing
      • An image processing operation typically defines a new image g in terms of an existing image f.
      • We can transform either the range of f .
      • Or the domain of f :
      • What kinds of operations can each perform?
    • 67. Point Processing
      • The simplest kind of range transformations are these independent of position x,y:
      • g = t(f)
      • This is called point processing.
      • What can they do?
      • What’s the form of t ?
      • Important: every pixel for himself – spatial information completely lost!
    • 68. Image Histograms
    • 69. Cumulative Histograms
    • 70. Histogram Matching
    • 71. Match-histogram code
    • 72. Neighborhood Processing (filtering)
      • Q: What happens if I reshuffle all pixels within the image?
      • A: It’s histogram won’t change. No point processing will be affected…
      • Need spatial information to capture this.
    • 73. Programming Assignment #1
      • Easy stuff to get you started with Matlab
        • James will hold tutorial this week
      • Distance Functions
        • SSD
        • Normalized Correlation
      • Bells and Whistles
        • Point Processing (color?)
        • Neighborhood Processing
        • Using your data (3 copies!)
        • Using your data (other images)

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