CIS 601

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CIS 601 Image Fundamentals Longin Jan Latecki
Slides by Dr. Rolf Lakaemper
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Fundamentals
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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Fundamentals
In the beginning well 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 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

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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

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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 -gt 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
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Remember the value of the cells is the
illumination (or brightness)
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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 well 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