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CIS 595

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CIS 595 Image Fundamentals Dr. Rolf Lakaemper Fundamentals Typical sensor for images: CCD Array (Charge Couple Devices) Use in digital cameras Typical resolution 1024 ... – PowerPoint PPT presentation

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Title: CIS 595


1
CIS 595 Image Fundamentals
Dr. Rolf Lakaemper
2
Fundamentals
Parts of these slides base on the
textbook Digital Image Processing by
Gonzales/Woods Chapters 1 / 2
3
Fundamentals
These slides show basic concepts about digital
images
4
Fundamentals
In the beginning well have a look at the
human eye
5
Fundamentals

6
Fundamentals
  • We are mostly interested in the retina
  • consists of cones and rods
  • Cones
  • color receptors
  • About 7 million, primarily in the retinas
    central portion
  • for image details
  • Rods
  • Sensitive to illumination, not involved in color
    vision
  • About 130 million, all over the retina
  • General, overall view

7
Fundamentals
Distribution of cones and rods
8
Fundamentals
The human eye is sensible to electromagnetic
waves in the visible spectrum
9
Fundamentals
The human eye is sensible to electromagnetic
waves in the visible spectrum , which is around
a wavelength of 0.000001 m 0.001 mm
10
Fundamentals
  • The human eye
  • Is able to perceive electromagnetic waves in a
    certain spectrum
  • Is able to distinguish between wavelengths in
    this spectrum (colors)
  • Has a higher density of receptors in the center
  • Maps our 3D reality to a 2 dimensional image !

11
Fundamentals
or more precise maps our continous (?)
reality to a (spatially) DISCRETE 2D image
12
Fundamentals
  • Some topics we have to deal with
  • Sharpness
  • Brightness
  • Processing of perceived visual information

13
Fundamentals
Sharpness The eye is able to deal with sharpness
in different distances
14
Fundamentals
Brightness The eye is able to adapt to different
ranges of brightness
15
Fundamentals
Processing of perceived information optical
illusions
16
Fundamentals
optical illusions Digital Image Processing does
NOT (primarily) deal with cognitive aspects of
the perceived image !
17
Fundamentals
What is an image ?
18
Fundamentals
The retinal model is mathematically hard to
handle (e.g. neighborhood ?)
19
Fundamentals
Easier 2D array of cells, modelling the
cones/rods
Each cell contains a numerical value (e.g.
between 0-255)
20
Fundamentals
  • The position of each cell defines the position of
    the receptor
  • The numerical value of the cell represents the
    illumination received by the receptor

5
7
1
0
12
4



21
Fundamentals
  • With this model, we can create GRAYVALUE images
  • Value 0 BLACK (no illumination / energy)
  • Value 255 White (max. illumination / energy)

22
Fundamentals
A 2D grayvalue - image is a 2D -gt 1D function,
v f(x,y)
23
Fundamentals
As we have a function, we can apply operators to
this function, e.g. H(f(x,y)) f(x,y) / 2
Operator
Image ( function !)
24
Fundamentals
H(f(x,y)) f(x,y) / 2
6
8
2
0
3
4
1
0
12
200
20
10
6
100
10
5
25
Fundamentals
Remember the value of the cells is the
illumination (or brightness)
6
8
2
0
3
4
1
0
12
200
20
10
6
100
10
5
26
Fundamentals
As we have a function, we can apply operators to
this function but why should we ? some
motivation for (digital) image processing
27
Fundamentals
  • Transmission of images

28
Fundamentals
  • Image Enhancement

29
Fundamentals
  • Image Analysis / Recognition

30
Fundamentals
The mandatory steps Image Acquisition and
Representation
31
Fundamentals
Acquisition
32
Fundamentals
Acquisition
33
Fundamentals
Acquisition
34
Fundamentals
  • Typical sensor for images
  • CCD Array (Charge Couple Devices)
  • Use in digital cameras
  • Typical resolution 1024 x 768 (webcam)

35
Fundamentals
CCD
36
Fundamentals
CCD
37
Fundamentals
CCD 3.2 million pixels !
38
Fundamentals
Representation The Braun Tube
39
Fundamentals
Representation Black/White and Color
40
Fundamentals
Color Representation Red / Green / Blue Model
for Color-tube Note RGB is not the ONLY
color-model, in fact its use is quiet
restricted. More about that later.
41
Fundamentals
Color images can be represented by 3D Arrays
(e.g. 320 x 240 x 3)
42
Fundamentals
But for the time being well handle 2D grayvalue
images
43
Fundamentals
Digital vs. Analogue Images Analogue Function
v f(x,y) v,x,y are REAL Digital Function
v f(x,y) v,x,y are INTEGER
44
Fundamentals
Stepping down from REALity to INTEGER coordinates
x,y Sampling
45
Fundamentals
Stepping down from REALity to INTEGER grayvalues
v Quantization
46
Fundamentals
Sampling and Quantization
47
Fundamentals
MATLAB demonstrations of sampling and
quantization effects
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