CIS 601
Image Fundamentals
Longin Jan Latecki
Slides by Dr. Rolf Lakaemper
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Parts of these slides base on the
textbook
Digital Image Processing
by Gonzales/Woods
Chapters 1 / 2
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Today we will
• Learn some basic concepts about digital
images (Textbook chapters 1 / 2)
• Show how MATLAB can help in
understanding these concepts
• Build a simple video – surveillance system
using MATLAB !
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In the beginning…
we’ll have a look at the human eye
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We are mostly interested in the retina:
• consists of cones and rods
•
•
Cones
• color receptors
• About 7 million, primarily in the retina’s
central portion
for image details
•
Rods
• Sensitive to illumination, not involved in
•
•
color vision
About 130 million, all over the retina
General, overall view
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Distribution of cones and rods:
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The human eye is sensible to electromagnetic waves in the
‘visible spectrum’ :
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The human eye is sensible to
electromagnetic waves in the ‘visible
spectrum’ , which is around a wavelength
of
0.000001 m = 0.001 mm
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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 !
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…or more precise:
maps our continous (?) reality to a
(spatially) DISCRETE 2D image
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Some topics we have to deal with:
•
•
•
Sharpness
Brightness
Processing of perceived visual
information
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Sharpness
The eye is able to deal with
sharpness in different distances
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Brightness
The eye is able to adapt to different
ranges of brightness
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Processing of perceived
information: optical illusions
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optical illusions:
Digital Image Processing does NOT
(primarily) deal with cognitive
aspects of the perceived image !
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What is an image ?
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The retinal model is mathematically hard to
handle (e.g. neighborhood ?)
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Easier: 2D array of cells, modelling the
cones/rods
Each cell contains a numerical value (e.g.
between 0-255)
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•
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 ………
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•
With this model, we can create GRAYVALUE
images
•
Value = 0: BLACK (no illumination / energy)
•
Value = 255: White (max. illumination / energy)
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A 2D grayvalue - image is a 2D -> 1D
function,
v = f(x,y)
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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 !)
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H(f(x,y)) = f(x,y) / 2
6
8
2
0
3
12 200 20
10
6
4
1
0
100 10
5
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Remember: the value of the cells is the
illumination (or brightness)
6
8
2
0
3
12 200 20
10
6
4
1
0
100 10
5
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As we have a function, we can apply
operators to this function…
…but why should we ?
some motivation for (digital) image
processing
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•
Transmission of images
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•
Image Enhancement
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•
Image Analysis / Recognition
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The mandatory steps:
Image Acquisition and
Representation
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Acquisition
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Acquisition
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Typical sensor for images:
CCD Array (Charge Couple Devices)
• Use in digital cameras
• Typical resolution 1024 x 768
(webcam)
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CCD
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CCD
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CCD:
3.2 million pixels !
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Representation
The Braun Tube
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Representation
Black/White and Color
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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.
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Color images can be represented by
3D Arrays (e.g. 320 x 240 x 3)
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But for the time
being we’ll handle
2D grayvalue
images
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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
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Stepping down from REALity to INTEGER
coordinates x,y: Sampling
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Stepping down from REALity to INTEGER
grayvalues v : Quantization
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Sampling
and
Quantization
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MATLAB demonstrations of sampling and
quantization effects in sampling.m