CIS 601 Image Fundamentals Longin Jan Latecki

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 !

Fundamentals

…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

Fundamentals

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

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

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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.

Fundamentals

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

Fundamentals

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