DigitalImageProcessing(DIP):

Introduc6onandFundamentals

A

Prof.PascalMatsakis

DIP:Introduc-onandFundamentals I.OriginsofDIP

Prof.PascalMatsakis

Digitalpictureproducedin1921

OriginsofDIP I.1.NewspaperIndustry(1920s)

3

Prof.PascalMatsakis

Firstpictureofthemoon

byaUSspacecra@in1964

OriginsofDIP I.2.SpaceProgram(1960s)

4

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DIP:Introduc-onandFundamentals II.InterestinDIP

Prof.PascalMatsakis

Applica6onsinmanyfields:

medicine,geography,physics,astronomy,defense…

Examples:

characterrecogniEon,fingerprintrecogniEon,

targetrecogniEon,weatherpredicEon,crop

assessment,detecEonofbonefractures,

detecEonofbraintumors,studyof

high-energyplasmas…

InterestinDIP II.1.Overview

6

Prof.PascalMatsakis

II.2a.ImagingintheElectromagne6cSpectrum

7InterestinDIP

λν =c, E=hνEnergyEFrequencyνWavelengthλ

RADIO WAVES

MICROWAVES

INFRARED ULTRAVIOLET

VISI

BLE

GAMMA RAYS SOFT X-RAYS

HARD X-RAYS

400nm500nm600nm700nm

Prof.PascalMatsakis

Bonescan

(tolocalizeinfecEonsandtumors)

II.2b.ImagingintheElectromagne6cSpectrum

8InterestinDIP

Gamma-Ray

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II.2c.ImagingintheElectromagne6cSpectrum

Imageofacircuitboard

(todetectflaws)

9InterestinDIP

X-Ray

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Fluorescencemicroscopeimagesofcorn

(tofindifcornisinfectedbysmut)

II.2d.ImagingintheElectromagne6cSpectrum

10InterestinDIP

Ultraviolet

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II.2e.ImagingintheElectromagne6cSpectrum

Imageofpapercurrency

(totrackandidenEfybills)

11InterestinDIP

VisualSpectrum

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SatelliteimageofNorthAmerica

(globalinventoryofhumanseRlements)

II.2f.ImagingintheElectromagne6cSpectrum

12InterestinDIP

Infrared

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MRIimageofahumanknee

(diagnosisofsport-relatedinjuries)

II.2g.ImagingintheElectromagne6cSpectrum

13InterestinDIP

Radio-Band

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II.2h.ImagingintheElectromagne6cSpectrum

14InterestinDIP

480-710nm

Panchroma6c

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440-490nm

520-570

II.2i.ImagingintheElectromagne6cSpectrum

15InterestinDIP

630-740

Colour

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450-515nm

525-605

630-690

II.2j.ImagingintheElectromagne6cSpectrum

16InterestinDIP

775-900

1550-1750

10400-12500

2090-2350

Prof.PascalMatsakis

450-515nm

525-605

630-690

II.2k.ImagingintheElectromagne6cSpectrum

17InterestinDIP

775-900

1550-1750

10400-12500

2090-2350

FalseColour

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II.3a.OtherImagingModali6es

Imageofanunbornbaby

(todeterminethehealthofhisdevelopment)

18InterestinDIP

Ultrasound

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II.3b.OtherImagingModali6es

19InterestinDIP

Computer-generatedimageofahumanskull

(forcriminalforensics)

Synthe6c

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DIP:Introduc-onandFundamentals III.MainStepsinDIP

Prof.PascalMatsakis

IMAG

E PR

EPRO

CESS

ING

L O W L E V E L

H I G H L E V E L

outputsareimages

5 REPRESENTATION AND DESCRIPTION

outputsareimageaRributes

III.1a.Overview

21MainStepsinDIP

IMAGE RESTORATION

2

IMAGE ENHANCEMENT

3

KNOWLEDGE BASE

1 IMAGE ACQUISITION

4 SEGMENTATION

6 OBJECT RECOGNITION

Prof.PascalMatsakis

LOWLEVEL:

acquisiEonandimprovement

ofpictorialinformaEonfor

humaninterpretaEon

andanalysis

HIGHLEVEL:

autonomousmachinepercepEon

III.1b.Overview

22MainStepsinDIP

Prof.PascalMatsakis

DIPcharacterizedbyspecificsoluEons.

III.1c.Overview

23MainStepsinDIP

Techniquesthatworkwellinonearea

canbetotallyinadequateinanother.

ActualsoluEonofaspecificproblem

generallysEllrequiressignificant

researchanddevelopment.

Prof.PascalMatsakis

III.2a.Examples

24MainStepsinDIP

Whichstep?

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III.2b.Examples

25MainStepsinDIP

Whichstep?

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III.2c.Examples

26MainStepsinDIP

Whichstep?

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Whichstep?

III.2d.Examples

27MainStepsinDIP

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DIP:Introduc-onandFundamentals IV.ImageDefini6onandRepresenta6on

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ImageDefini-onandRepresenta-onIV.1a.AnalogImage

29

fa | 2 → R [0;+∞[ funcEon

funcEon

fa | 3 → R R

image

fa | 2 → R R3 funcEon

funcEon

fa | 2 → R C

fa|→ isatypicalcase;R2 [0;+∞[

fa(x,y)istheintensityoff

aat(x,y).

Prof.PascalMatsakis

x

y

ImageDefini-onandRepresenta-onIV.1b.AnalogImage

30

Considerfa|→R2 [0;+∞[

z=fa(x,y)

x

y

z

M

N

0

L

DomainofdefiniEon[0,M]x[0,N]andrange[0,L]

y

x

M

N0

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ImageDefini-onandRepresenta-onIV.1c.AnalogImage

31

fa

ZeroPadding

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ImageDefini-onandRepresenta-onIV.1d.AnalogImage

32

fa

CircularIndexing

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ImageDefini-onandRepresenta-onIV.1e.AnalogImage

33

ReflectedIndexing

fa

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ImageDefini-onandRepresenta-onIV.2a.DigitalImage

34

((x,y),f(x,y))

f | Z 2 → 0..+∞

funcEon

funcEon

f | Z 3 → Z image

funcEon

f | Z 2 → Z3

f|→ isatypicalcaseZ2 0..+∞ :grayscaleimage

loca6on graylevel pixel

Prof.PascalMatsakis

f(0,0)f(0,1)…f(0,N-1)

f(1,0)

. .. .. .

f(M-1,0)…f(M-1,N-1)

ImageDefini-onandRepresenta-onIV.2b.DigitalImage

35

0

1

2

12...y...N-1

0≤f(x,y)≤L-1

M-1

x

...

...

pixellocaEon

Considerf|→Z2 0..+∞

Assumerangeincludedin0..L-1:

,i.e.,l -bitgrayscaleimage typically,L=2l

AssumedomainofdefiniEonis0..M-1x0..N-1:

Prof.PascalMatsakis

ImageDefini-onandRepresenta-onIV.2c.DigitalImage

36

y

xpixel

f(0,0)f(0,1)…f(0,N-1)

f(1,0)

. .. .. .

f(M-1,0)…f(M-1,N-1)

Considerf|→Z2 0..+∞

Assumerangeincludedin0..L-1:

,i.e.,l -bitgrayscaleimage typically,L=2l

AssumedomainofdefiniEonis0..M-1x0..N-1:

Prof.PascalMatsakis

ImageDefini-onandRepresenta-onIV.3a.Digi6za6on

37

analog

digital

digi6za6on

sampling

quan6za6on

0..+∞

fa | 2 → R [0;+∞[

f | →

Z 2

→ [0;+∞[ R

2 → 0..+∞

Z 2

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1024 512 256

128 64 32

sampling

ImageDefini-onandRepresenta-onIV.3b.Digi6za6on

38

Checkerboardeffect:

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32 16 8

4 2

quanEzaEon

ImageDefini-onandRepresenta-onIV.3c.Digi6za6on

39

False

contouring:

Prof.PascalMatsakis