ImageProcessing2-ImageProcessingFundamentals

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Course Website:http://www.comp.dit.ie/bmacnamee

Digital Image Processing:

Digital Imaging Fundamentals

Brian Mac [email protected]
http://www.comp.dit.ie/bmacnameemailto:[email protected]:[email protected]://www.comp.dit.ie/bmacnamee


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42Contents

This lecture will cover: The human visual system

Light and the electromagnetic spectrum

Image representation Image sensing and acquisition

Sampling, quantisation and resolution


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42Human Visual System

The best vision model we have!Knowledge of how images form in the eye

can help us with processing digital images

We will take just a whirlwind tour of thehuman visual system


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42Structure Of The Human Eye

The lens focuses light from objects onto theretina

The retina is covered with

light receptors calledcones (6-7 million) androds (75-150 million)

Cones are concentrated

around the fovea and arevery sensitive to colour

Rods are more spread out

and are sensitive to low levels of illuminationImagestakenfromGonzalez&Woods,

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42Blind-Spot Experiment

Draw an image similar to that below on apiece of paper (the dot and cross are about

6 inches apart)

Close your right eye and focus on the cross

with your left eyeHold the image about 20 inches away from

your face and move it slowly towards you

The dot should disappear!


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42Image Formation In The Eye

Muscles within the eye can be used tochange the shape of the lens allowing us

focus on objects that are near or far away

An image is focused onto the retina causingrods and cones to become excited which

ultimately send signals to the brain


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42Brightness Adaptation & Discrimination

The human visual system can perceiveapproximately 1010 different light intensity

levels

However, at any one time we can onlydiscriminate between a much smaller

numberbrightness adaptation

Similarly, theperceived intensityof a regionis related to the light intensities of the

regions surrounding it


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Brightness Adaptation & Discrimination

(cont)

An example of Mach bands

ImagestakenfromGonzalez&Woods,

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(cont)


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(cont)

An example ofsimultaneous contrast


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(cont)

For more great illusion examples take a look at: http://web.mit.edu/persci/gaz/
http://web.mit.edu/persci/gaz/http://web.mit.edu/persci/gaz/


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Available here: http://www.lottolab.org/Visual%20Demos/Demo%2015.html
http://www.lottolab.org/Visual%20Demos/Demo%2015.htmlhttp://www.lottolab.org/Visual%20Demos/Demo%2015.html


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42Optical Illusions

Our visualsystems play lots

of interesting

tricks on us


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42Optical Illusions (cont)


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42Optical Illusions (cont)

Stare at the crossin the middle of

the image and

think circles


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Mind Map Exercise: Mind Mapping

For Note Taking

Beau Lotto: Optical Illusions Show How We Seehttp://www.ted.com/talks/lang/eng/beau_lotto_optical_illusions_show_how_we_see.html
http://www.ted.com/talks/lang/eng/beau_lotto_optical_illusions_show_how_we_see.htmlhttp://www.ted.com/talks/lang/eng/beau_lotto_optical_illusions_show_how_we_see.htmlhttp://www.ted.com/talks/lang/eng/beau_lotto_optical_illusions_show_how_we_see.htmlhttp://www.ted.com/talks/lang/eng/beau_lotto_optical_illusions_show_how_we_see.html


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Light And The Electromagnetic

Spectrum

Light is just a particular part of theelectromagnetic spectrum that can be

sensed by the human eye

The electromagnetic spectrum is split upaccording to the wavelengths of different

forms of energy


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42Reflected Light

The colours that we perceive are determinedby the nature of the light reflected from an

object

For example, if whitelight is shone onto a

green object most

wavelengths areabsorbed, while green

light is reflected from

the object

Colours

Absorbed


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42Sampling, Quantisation And Resolution

In the following slides we will consider whatis involved in capturing a digital image of a

real-world scene

Image sensing and representation Sampling and quantisation

Resolution


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42Image Representation

col

row

f (row, col)

Before we discuss image acquisition recallthat a digital image is composed ofM rowsand Ncolumns of pixelseach storing a value

Pixel values are mostoften grey levels in therange 0-255(black-white)

We will see later onthat images can easilybe represented asmatrices

Im

agestakenfromGonzalez&Woods,

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42Image Acquisition

Images are typically generated byilluminatinga scene and absorbing the

energy reflected by the objects in that scene

Typical notions ofillumination and

scene can be way off:

X-rays of a skeleton

Ultrasound of anunborn baby

Electro-microscopic

images of molecules

Im


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42Image Sensing

Incoming energy lands on a sensor materialresponsive to that type of energy and this

generates a voltage

Collections of sensors are arranged tocapture images

Imaging Sensor

Line of Image Sensors Array of Image SensorsIm


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42Image Sensing

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Using Sensor Strips and Rings


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42Image Sampling And Quantisation

A digital sensor can only measure a limitednumber ofsamples at a discrete set of

energy levels

Quantisation is the process of converting acontinuous analogue signal into a digital

representation of this signal

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I S li A d Q ti ti


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Image Sampling And Quantisation

(cont)

Remember that a digital image is alwaysonly an approximation of a real world

scene

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42Spatial Resolution

The spatial resolution of an image isdetermined by how sampling was carried out

Spatial resolution simply refers to the

smallest discernable detail in an image Vision specialists will

often talk about pixel

size

Graphic designers will

talk about dots per

inch (DPI)

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42Spatial Resolution (cont)

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42Intensity Level Resolution

Intensity level resolution refers to thenumber of intensity levels used to represent

the image The more intensity levels used, the finer the level of

detail discernable in an image

Intensity level resolution is usually given in terms of

the number of bits used to store each intensity level

Number of Bits

Number of Intensity

Levels Examples

1 2 0, 1

2 4 00, 01, 10, 11

4 16 0000, 0101, 1111

8 256 00110011, 01010101

16 65,536 1010101010101010

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42Intensity Level Resolution (cont)

128 grey levels (7 bpp) 64 grey levels (6 bpp) 32 grey levels (5 bpp)

16 grey levels (4 bpp) 8 grey levels (3 bpp) 4 grey levels (2 bpp) 2 grey levels (1 bpp)

256 grey levels (8 bits per pixel)

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42Saturation & Noise

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42Resolution: How Much Is Enough?

The big question with resolution is alwayshow much is enough?

This all depends on what is in the image and

what you would like to do with it

Key questions include

Does the image look aesthetically pleasing?

Can you see what you need to see within the

image?

53 Resolution: How Much Is Enough?


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Resolution: How Much Is Enough?

(cont)

The picture on the right is fine for countingthe number of cars, but not for reading the

number plate

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I i L l R l i ( )


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Low Detail Medium Detail High Detail

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I t it L l R l ti ( t )


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42Summary

We have looked at: Human visual system

Light and the electromagnetic spectrum

Image representation Image sensing and acquisition

Sampling, quantisation and resolution

Next time we start to look at techniques forimage enhancement

ImageProcessing2-ImageProcessingFundamentals

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