DIGITAL IMAGE PROCESSING BASIC

8/11/2019 DIGITAL IMAGE PROCESSING BASIC

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Applications and currentApplications and currenttrends in Image processingtrends in Image processing


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OverviewOverview

Image Concepts and Structures

Compression Techniques

Applications in image processing


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Image Concepts andImage Concepts and

StructuresStructuresDigital imageA 2D array of samples (pixels)

Leel of intensity ! num"er of "its per pixelClassification#inary images

Computer graphics$rayscale images

Color images


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%hy do &e process images'%hy do &e process images'

ACILITAT *ICT+, ST-,A$

./A.C0.T A.D ,ST-,ATI-.

1IS+ALIATI-. S$0.TATI-.

C-0*,SSI-.


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APPLICATIONS IN IMAGEAPPLICATIONS IN IMAGE

PROCESSINGPROCESSING

ST$A.-$,A*/3

ST$A.AL3SIS

DI$ITAL %AT, 0A,4I.$

I0A$ C-0*,SSI-.

I0A$ S$0.TATI-.

I0A$ ,C-$.ITI-.

I0A$ ./A.C0.T


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STEGANOGRAP!STEGANOGRAP!

/IDI.$ A DATA I.SID A I0A$

-riginal essel

image

m"edded essel image


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STEGANAL!SISSTEGANAL!SIS

TC/.I6+ - DTCTI-. -

ST$- I0A$S


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%igital water mar&ing%igital water mar&ing

T&o methods for copyright protection

5 CryptographyTransform digital contents into unperceia"le

format

ncryption7Decryption

5 %atermar8ing/ide the copyright control information into digital

content "y modification the content itself


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$eneric &atermar8 em"edding scheme$eneric &atermar8 em"edding scheme

$eneric &atermar8 recoery scheme


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&atermar8ed image ,ecoered correct image

-riginal imageWatermarking technique


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'() %o 'e Need Compression*'() %o 'e Need Compression*

T- ,D+C T/ 00-,3

ICI.T +S - #A.D%IDT/

T&o types of compression

Lossy compression Loss less compression


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Loss) CompressionLoss) Compression

LOSSY COMPRESSION

HIGH COMPRESSION RATIO

REMOVING THE INORMATION ROM THE SIGNAL

INORMATION LOSS

*redictie (0otion Compensation)

requency -riented (Transform9 Su":"and)

Importance -riented (iltering9 #it Allocation9 Sampling9

6uanti;ation)/y"rid ()


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Loss less compressionLoss less compression

LOSSLESS COMPRESSION

REMOVING THE RE!"N!ANT INORMATION

ROM THE IMAGE

LOW COMPRESSION RATIONO LOSS

/uffman Coding

Arithmetic CodingLempel i Algorithm

,un:length Coding


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T)pes o+ compressionT)pes o+ compression

algorit(msalgorit(msDCT

D%T

S*I/T

1CT-, 6+A.TIATI-.

,ACTAL C-0*,SSI-.


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"loc& Trans+orm Encoding"loc& Trans+orm Encoding

ig:;ag 6uanti;e

,un:lengthCode

/uffmanCode

011010001011101...

DCT


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139 144 149 153

144 151 153 156

150 155 160 163

159 161 162 160

original image

DCT

1260 -1 -12 -5

-23 -17 -6 -3-11 -9 -2 2

-7 -2 0 1

AC components

6uanti;e

;ig;ag79 0 -2 -1 -1 -1 0 0 -1 0 0 0 0 0 0 0

run:lengthcode

/uffmancode

10011011100011...

DC component

Block

Encoding


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,PEG COMPRESSION,PEG COMPRESSION

DCT 6uanti;er ntropy

ncoder

6:Ta"lencoding

Ta"le

xxxxxxx

xxxxxxx

xxxxxxx


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COMPRESSION RATIO -$.#COMPRESSION RATIO -$.#

#efore compression After compression


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"loc& Trans+orm Encoding"loc& Trans+orm Encoding

ig:;ag 6uanti;e

,un:lengthCode

/uffmanCode

011010001011101...

DCT


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139 144 149 153

144 151 153 156

150 155 160 163

159 161 162 160

original image

DCT

1260 -1 -12 -5

-23 -17 -6 -3-11 -9 -2 2

-7 -2 0 1

AC components

6uanti;e

;ig;ag79 0 -2 -1 -1 -1 0 0 -1 0 0 0 0 0 0 0

run:lengthcode

/uffmancode

10011011100011...

DC component

Block

Encoding


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Image recognitionImage recognition


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Image recognitionImage recognition


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Need +or "io metric s)stemNeed +or "io metric s)stem

/ac8ers in cryptography

#iometric data is unique and cannot "e

transferred

xtremely difficult to duplicate


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Identi+ication vs veri+icationIdenti+ication vs veri+ication

1erification (one to one)

g pass&ord erification

Identification (-ne to many)

g face recognition

.eed for high speed processors to performcomputation


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"iometric tec(nolog)"iometric tec(nolog)

AC ,C-$.ITI-.

I.$, *,I.T ,C-$.ITI-.

I,IS ,C-$.ITI-.

,TI.A SCA.


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'or&ing o+ a /iometric s)stem'or&ing o+ a /iometric s)stem

"LOC0 %IAGRAM 1OR 1ACE"LOC0 %IAGRAM 1OR 1ACE


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"LOC0 %IAGRAM 1OR 1ACE"LOC0 %IAGRAM 1OR 1ACE

RECOGNITIONRECOGNITION

Input face image

ace detection

ace feature

extraction

eature 0atching Decision ma8er

-utput result

Face

database

Facerecognition


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T(eor) "e(indT(eor) "e(ind

1acial Recognition1acial Recognitionigenface Technology

Local 1eature Anal)sis. 2345$/loc&s


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T(eor) "e(indT(eor) "e(ind

1acial Recognition1acial Recognition


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Iris Anatom) and P()siolog)Iris Anatom) and P()siolog)

-nly internal organ that isnormally isi"le externally?

Isolated and protectedSurgical ris8s ofmodifications

Tra"ecular mesh&or8 of

connectie tissue


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Iris Scan ProcessIris Scan Process

0a@or Steps

Isolation of iris tissueimage

eature encoding

using 2D quadrature$a"or %aeletdemodulation

Statistical Analyses


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Iris Image IsolationIris Image Isolation

0inimum B pixels

BB:>B more typical

0onochrome cameras5 1isi"le light

5 Infrared light BB 5 BB nm

Textures reealed

Integrodifferential-perator

*upillary and outer

"oundary$aussian smoothing

operator


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Ga/or 'avelet %emodulationGa/or 'avelet %emodulation

6uadrature 2D $a"or&aelets5 +sed to encode iris features

5 -"tain iris code5 29B>E phase "its computed

(2F= "ytes)

5 *hase information

5 Amplitude information

depends on too manyfactors Contrast9 illumination9

etcG


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More Ga/or6More Ga/or6

(xB9yB) specify &aelet position

specify &aelet length

specify a modulation &ae ector &hich can

"e interpreted as polar coordinates as spatial

frequency

( ) ( ) ( ) ( ) H2H77 BBBB22

B22

B)9(yyvxxuiyyxx

eeyx ++

=

)9(


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StatisticsStatistics

The key to iis eco!nition

is the "#i$ue o" # test o"

st#tistic#$ in%epen%ence.

&1' *ass 5 different eyes

ail 5 same eyes

/amming Distance (/D)

computation

5 0easure of dissimilarity

J-,

5 Detects Disagreement

A.D

5 *rotects against Corruption

A"out BB9BBB irises7sec

on a KBB 0/; C*+


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1inger print veri+ication s)stem1inger print veri+ication s)stem


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1eature e7traction1eature e7traction


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1eature En(ancement1eature En(ancement

Origina# Enhance$


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Aut%matic Minutiae !etecti%n&

0inutiae are essentially

terminations and "ifurcations

of the ridge lines that constitute

a fingerprint pattern


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Matc(ing algorit(mMatc(ing algorit(m

Input image Data "ase

Comparision of minutiae detection


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recognitionrecognition

E'i(ting S)(tem&

BBM A, N M ,, (depends on

ealuation scheme)


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Arti*icia##)create$

+i%metric(

Attack atthe

!ata,a(e

Attac&sAttac&s

Attacking

Via In-ut

P%rt


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Multi/iometrics S)stemsMulti/iometrics S)stems

mu#ti-#e ,i%metric trait(m%re re#ia,#eanti.(-%%*ing mea(ure(a$$re(( the -r%,#em %* n%n.uni/er(a#it)


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Image segmentationImage segmentation

Segmentation9 is to distinguish o"@ects from

"ac8ground


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IMAGE SEGMENTATIONIMAGE SEGMENTATION

ALGORITMSALGORITMS CA..3 D$ DTCTI-. 0T/-D

+3

+3 0A.S

4:0A.S

#*.


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"loc& diagram +or"loc& diagram +or

segmentationsegmentation


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Input imageInput image


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Preprocessed imagePreprocessed image


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Region grown imageRegion grown image


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Edge detected imageEdge detected image


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Segmented /) +u88) logicSegmented /) +u88) logic


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Segmented /) "PNSegmented /) "PN


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Segmentation /) single seedSegmentation /) single seed

pointpoint


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Minimum 3 seed pointsMinimum 3 seed points

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DIGITAL IMAGE PROCESSING BASIC