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CIS 595
Image Fundamentals
Dr. Rolf Lakaemper
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Fundamentals
Parts of these slides base on the textbook
Digital Image Processingby Gonzales/Woods
Chapters 1 / 2
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Fundamentals
These slides show
basic concepts about digital images
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Fundamentals
In the beginning…
we’ll have a look at the human eye
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Fundamentals
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Fundamentals
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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Fundamentals
Distribution of cones and rods:
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Fundamentals
The human eye is sensible to electromagnetic waves in the ‘visible spectrum’ :
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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
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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 !
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Fundamentals
…or more precise:
maps our continous (?) reality to a (spatially) DISCRETE 2D image
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Fundamentals
Some topics we have to deal with:
• Sharpness• Brightness
• Processing of perceived visual information
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Fundamentals
Sharpness
The eye is able to deal with sharpness in different distances
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Fundamentals
Brightness
The eye is able to adapt to different ranges of brightness
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Fundamentals
Processing of perceived information: optical illusions
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Fundamentals
optical illusions:
Digital Image Processing does NOT (primarily) deal with cognitive
aspects of the perceived image !
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Fundamentals
What is an image ?
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Fundamentals
The retinal model is mathematically hard to handle (e.g. neighborhood ?)
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Fundamentals
Easier: 2D array of cells, modelling the cones/rods
Each cell contains a numerical value (e.g. between 0-255)
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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 ………
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Fundamentals
• With this model, we can create GRAYVALUE images
• Value = 0: BLACK (no illumination / energy)
• Value = 255: White (max. illumination / energy)
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Fundamentals
A 2D grayvalue - image is a 2D -> 1D function,
v = f(x,y)
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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 !)
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Fundamentals
H(f(x,y)) = f(x,y) / 2
6 8 2 0
12 200 20 10
3 4 1 0
6 100 10 5
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Fundamentals
Remember: the value of the cells is the illumination (or brightness)
6 8 2 0
12 200 20 10
3 4 1 0
6 100 10 5
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Fundamentals
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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Fundamentals
• Transmission of images
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Fundamentals
• Image Enhancement
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Fundamentals
• Image Analysis / Recognition
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Fundamentals
The mandatory steps:
Image Acquisition and Representation
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Fundamentals
Acquisition
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Fundamentals
Acquisition
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Fundamentals
Acquisition
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Fundamentals
Typical sensor for images:
CCD Array (Charge Couple Devices)
• Use in digital cameras• Typical resolution 1024 x 768
(webcam)
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Fundamentals
CCD
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Fundamentals
CCD
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Fundamentals
CCD: 3.2 million pixels !
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Fundamentals
Representation
The Braun Tube
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Fundamentals
Representation
Black/White and Color
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Fundamentals
Color Representation: Red / Green / Blue
Model forColor-tube
Note: RGB is not the ONLY color-model, in fact its use is quiet restricted. More about that later.
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Fundamentals
Color images can be represented by3D Arrays (e.g. 320 x 240 x 3)
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Fundamentals
But for the time being we’ll handle
2D grayvalue images
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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
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Fundamentals
Stepping down from REALity to INTEGER coordinates x,y: Sampling
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Fundamentals
Stepping down from REALity to INTEGER grayvalues v : Quantization
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Fundamentals
Samplingand
Quantization
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Fundamentals
MATLAB demonstrations of sampling and quantization effects