CIS 601Image Fundamentals

Longin Jan Latecki

Slides by Dr. Rolf Lakaemper

Fundamentals

Parts of these slides base on the textbook

Digital Image Processingby Gonzales/Woods

Chapters 1 / 2

Fundamentals

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 !

Fundamentals

In the beginning…

we’ll have a look at the human eye

Fundamentals

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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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

12 200 20 10

3 4 1 0

6 100 10 5

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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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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 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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Color images can be represented by3D 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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Samplingand

Quantization

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MATLAB demonstrations of sampling and quantization effects in sampling.m