Pengantar Pengolahan Citra Digital (KOM 421) – 3(2-3)

(1)

Kuliah 01:

Pendahuluan

Yeni Herdiyeni

Departemen Ilmu Komputer IPB

Semester Ganjil 2008

Pengantar Pengolahan Citra Digital

(KOM 421)

–

3(2-3)

Departemen Ilmu Komputer -IPB

Topik

•

Tujuan Instruksional Umum:

Mahasiswa mampu menjelaskan, mengolah dan

menganalisis citra digital.

•

Deskripsi:

(2)

Buku Bacaan:

•

Gonzalez, R. C., Woods, R. E., Eddins, Steven. 2004.

Digital Image

Processing Using Matlab

. Prentice Hall. (BUKU UTAMA)

•

Alasdair McAndrew. 2004.

Introduction to Digital Image Processing with

Matlab

. Thomson Course Technology, USA.

•

Acharya, Tinku dan Ray, A.K. 2005.

Image Processing. Principles and

Applications

. A John Wiley and Sons, Inc., Publication

•

Russ, John. C. 2007.

The Image Processing Handbook, Fifth Edition

. Taylor

& Francis Group, LLC

•

Umbaugh, S.C. 1999.

Computer Vision and Image Processing. A Practical

Approach using CVI Tools

. Prentice Hall PTR.

•

Rastislav Lukac dan Konstantinos. 2007.

Color Image Processing. Methods

and Applications

. Taylor & Francis Group, LLC

•

Pitas, I. Digital Image Processing Algorithm. 1993. Prentice Hall

•

Bahan bacaan lain yang relevan

Pengajar

•

Yeni Herdiyeni

•

Aziz Kustiyo

•

Sony Hartono (Praktikum)

Komponen Penilaian

•

UTS

•

UAS

•

Tugas

•

Quiz

(3)

Materi Kuliah

•

Pertemuan 1 : Pendahuluan

•

Pertemuan 2 : Citra Digital dan Matlab

•

Pertemuan 3 : Pengolahan Titik

•

Pertemuan 4 : Restorasi Citra

•

Pertemuan 5 : Image Enhancement

•

Pertemuan 6 : Pengolahan Warna

•

Pertemuan 7 : Transformasi Citra pada ruang

frekuensi (

Fourier Transformation

)

•

Ujian Tengah Semester

Materi Kuliah #2

•

Pertemuan 8 : Transformasi Citra pada ruang frekuensi

(Wavelet Transformation)

•

Pertemuan 9 : Deteksi tepi (

edge detection

)

•

Pertemuan 10 : Segmentasi Citra

•

Pertemuan 11 : Morfologi Citra

•

Pertemuan 12 : Pemampatan Citra (

Image Compression

–

RLE, Huffman Code

)

•

Pertemuan 13 : Pemampatan Citra JPEG

•

Pertemuan 14 : Pengenalan Pola (

Pattern Recognition

)

(4)

DIP

astronomy

seismology

inspection

autonomous

navigation

reconnassaince

& mapping

remote

sensing

surveillance

microscopy

radiology

robotic assembly

digital library

ultrasonic

imaging

radar,

SAR

meteorology

internet

Applications of Digital Image Processing (DIP)

From Prof. Alan C. Bovik

(5)

27 August 2008 9 1999-2007 by Richard Alan Peters II

Image Formation

Pengantar Pengolahan Citra Digital

Image Formation

projection

through lens

(6)

Image Formation

projection onto

discrete sensor

array.

digital camera

Image Formation

sensors register

average color.

(7)

Image Formation

continuous colors,

discrete locations.

discrete

real-valued image

Digital Image Formation: Quantization

continuous color input

d

is

cr

e

te

col

o

r o

u

tp

u

t

(8)

Sampling and Quantization

pixel grid

sampled

real image

quantized

sampled &

quantized

Digital Image

a grid of squares,

each of which

contains a single

color

each square is

called a pixel (for

picture element

)

(9)

original

+ gamma

- gamma

- brightness

+ brightness

original

+ contrast

- contrast

histogram EQ

histogram mod

Pengolahan Titik

original

blurred

sharpened

(10)

Spatial Filtering

bandpass

filter

unsharp

masking

original

Spatial Filtering

bandpass

filter

unsharp

masking

original

(11)

Motion Blur

vertical

regional

zoom

rotational

original

Pengantar Pengolahan Citra Digital

color noise

blurred image

color-only blur

(12)

5x5 Wiener filter

color noise

blurred image

Noise Reduction

original

periodic

noise

(13)

27 August 2008 1999-2007 by Richard Alan Peters II 25

Color Images

•

Are constructed from three

intensity maps.

•

Each intensity map is pro-jected

through a color filter (

e.g.,

red,

green, or blue, or cyan,

magenta, or yellow) to create a

monochrome image.

•

The intensity maps are overlaid

to create a color image.

•

Each pixel in a color image is a

three element vector.

Color

Image

s On a

(14)

27 August 2008 27 1999-2007 by Richard Alan

Peters II

Color Processing

requires some

knowledge of how

we see colors

Pengantar Pengolahan Citra Digital

Eye’s Light Sensors

(15)

Color Sensing / Color Perception

These are approximations of the responses to the visible spectrum of the

“red”, “green”, and “blue”

receptors of a typical human eye.

These are approximations of the responses to the visible spectrum of the

“red”, “green”, and “blue”

receptors of a typical human eye.

The simultaneous red + blue

response causes us to

perceive a continuous range

of hues on a circle. No hue is

greater than or less than any

other hue.

(16)

lu

m

in

a

n

ce

hue

sa

tu

ra

tio

n

photo receptors

brain

The eye has 3 types of photoreceptors:

sensitive to red, green, or blue light.

The brain transforms RGB into separate

brightness and color channels (

e.g.

, LHS).

Color Sensing / Color Perception

Color Perception

all bands

luminance

chrominance

red

green

blue

16

×

pixelization of:

(17)

all bands

luminance

chrominance

red

green

blue

Color Balance

and Saturation

Uniform changes in color

components result in change of

tint.

E.g.,

if all G pixel values are multiplied by

(18)

Color Transformations

        218 222 222         185 222 222         114 122 17         106 227 236         103 171 240         160 171 240         171 121 17         166 230 240                           171 121 17 114 122 17                           218 222 222 185 222 222                           160 171 240 103 171 240                           166 230 240 106 227 236

Image aging: a transformation,



, that mapped:

The 2D Fourier Transform of a Digital Image

 

1 1

 

2

0 0

,

ur vc i R C R C u v

I r c

u v e



 

 

  _ _

 

  

I

 

1 1 2

1

0 0

( , )

ur vc

R C i

R C RC

r c

u,v

I r c e



 

   _  _

 



 

I

Let I(r,c) be a single-band (intensity) digital image with R

rows and C columns. Then, I(r,c) has Fourier representation

where

are the R

x C

Fourier coefficients.

(19)

2D Sinusoids:

orientation

... are plane waves with

grayscale amplitudes, periods in

terms of lengths, ...

 









































sin

1

R

cos

C

2

cos

2

,









c

r

A

c

r

I

A





= phase shift

r

c

2D Sinusoids:

... specific orientations,

and phase shifts.

r

c

r

(20)

The Value of a Fourier Coefficient …

… is a complex

number with a

real part and an

imaginary part.

If you represent

that number as a

magnitude,

A

, and

a phase,



, …

..these represent the amplitude

and offset of

the

sinusoid with

frequency

w

and direction



.

(21)

I

|

F

{I}

|



[

F

{I}]

The Fourier Transform of an Image

magnitude

phase

Continuous Fourier Transform

The continuous Fourier

transform assumes a

continuous image exists

in a finite region of an

infinite plane.

 

 



 

 



 

 



u

v

e

dudv

c

r

i2(ucvr)

,

I



 

 



 

 



 

 



r

c

e

dcdr

v

u

i2 (ucvr)

,

I

,



I

(22)

27 August 2008 43 1999-2007 by Richard Alan

Peters II

Discrete Fourier Transform

The discrete Fourier

transform assumes a

digital image exists on a

closed surface, a torus.

 





        _    1 0 2 1 0

)

(

I

C u R vr C uc i R v

e

u,v

r,c

I



 







 

           1 0 2 1 0

,

I

,

C c R rv C cu i R r

e

c

r

v

u



I

The BoingBoing Bloggers

Departemen Ilmu Komputer -IPB

Convolution



r16,c16







r0,c0







r16,c16



 



r16,c16





r16,c16





Sum times 1/5

(23)

Convolution Property of the Fourier Transform

The Fourier Transform of a

product equals the convolution of

the Fourier Transforms. Similarly,

the Fourier Transform of a

convolution is the product of the

Fourier Transforms





.

by

computed

be

can

n

convolutio

spatial

a

Then,

tion

multiplica

pointwise

represents

n

convolutio

represents

.

}

{

M oreover,

.

}

{

Then,

).

,

(

and

)

,

(

Transforms

Fourier

have

)

,

(

and

)

,

(

functions

Let

1

G

F

g

f

G

F

g

f

G

F

g

f

v

u

G

v

u

F

c

r

g

c

r

f

-

















F

(24)

Boundary Detection

Finding the Corpus Callosum

(G. Hamarneh, T. McInerney, D. Terzopoulos)

Departemen Ilmu Komputer -IPB

Nonlinear Processing: Binary Morphology

“L” shaped SE

O marks origin

Foreground: white pixels

Background: black pixels

(25)

Image Compression

Yoyogi Park, Tokyo, October 1999. Photo by Alan Peters.

Original image is

5244w x 4716h @

1200 ppi:

127MBytes

Image Compression: JPEG

JP

E

G

q

u

al

ity

le

vel

Fi

le

si

ze

in b

y

(26)

JP

E

G

q

u

al

ity

le

vel

Fi

le

si

ze

in b

y

tes

Image Compression: JPEG

(27)

(28)

Pengantar Pengolahan Citra Digital (KOM 421) – 3(2-3)

Kuliah 01:

Deskripsi:

Applications

Rastislav Lukac dan Konstantinos. 2007.

Pengajar

Pertemuan 7 : Transformasi Citra pada ruang

Pertemuan 12 : Pemampatan Citra (

ultrasonic

Pengantar Pengolahan Citra Digital

Image Formation

pixel grid

original

unsharp

Pengantar Pengolahan Citra Digital

noise

to create a color image.

Pengantar Pengolahan Citra Digital

The simultaneous red + blue

The brain transforms RGB into separate

luminance

Color Transformations

Fourier coefficients.

and phase shifts.

frequency

The continuous Fourier

Departemen Ilmu Komputer -IPB

Fourier Transforms

Departemen Ilmu Komputer -IPB

127MBytes

Classification