cis 595 image fundamentals dr. rolf lakaemper. fundamentals parts of these slides base on the...

CIS 595

Image Fundamentals

Dr. Rolf Lakaemper

Fundamentals

Parts of these slides base on the textbook

Digital Image Processingby Gonzales/Woods

Chapters 1 / 2

Fundamentals

These slides show

basic concepts about digital images

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

Fundamentals

Distribution of cones and rods:

Fundamentals

The human eye is sensible to electromagnetic waves in the ‘visible spectrum’ :

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

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 !

Fundamentals

…or more precise:

maps our continous (?) reality to a (spatially) DISCRETE 2D image

Fundamentals

Some topics we have to deal with:

• Sharpness• Brightness

• Processing of perceived visual information

Fundamentals

Sharpness

The eye is able to deal with sharpness in different distances

Fundamentals

Brightness

The eye is able to adapt to different ranges of brightness

Fundamentals

Processing of perceived information: optical illusions

Fundamentals

optical illusions:

Digital Image Processing does NOT (primarily) deal with cognitive

aspects of the perceived image !

Fundamentals

What is an image ?

Fundamentals

The retinal model is mathematically hard to handle (e.g. neighborhood ?)

Fundamentals

Easier: 2D array of cells, modelling the cones/rods

Each cell contains a numerical value (e.g. between 0-255)

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 ………

Fundamentals

• With this model, we can create GRAYVALUE images

• Value = 0: BLACK (no illumination / energy)

• Value = 255: White (max. illumination / energy)

Fundamentals

A 2D grayvalue - image is a 2D -> 1D function,

v = f(x,y)

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 !)

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

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

Fundamentals

As we have a function, we can apply operators to this function…

…but why should we ?

some motivation for (digital) image processing

Fundamentals

• Transmission of images

Fundamentals

• Image Enhancement

Fundamentals

• Image Analysis / Recognition

Fundamentals

The mandatory steps:

Image Acquisition and Representation

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Acquisition

Fundamentals

Acquisition

Fundamentals

Acquisition

Fundamentals

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

Fundamentals

Representation

Black/White and Color

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.

Fundamentals

Color images can be represented by3D Arrays (e.g. 320 x 240 x 3)

Fundamentals

But for the time being we’ll handle

2D grayvalue images

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

Fundamentals

Stepping down from REALity to INTEGER coordinates x,y: Sampling

Fundamentals

Stepping down from REALity to INTEGER grayvalues v : Quantization

Fundamentals

Samplingand

Quantization

Fundamentals

MATLAB demonstrations of sampling and quantization effects