Kuliah 01 - Introduction

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Departemen Ilmu Komputer -IPBPengantar Pengolahan Citra Digital

Lecture 01:

Introduction

Yeni Herdiyeni

Dept of Computer Science IPB

Introduction to Digital Image Processing(KOM 421)3(2-3)


One picture is worth a thousand

words


Text Books:

Gonzalez, R. C., Woods, R. E., Eddins, Steven. 2004. Digital ImageProcessing Using Matlab. Prentice Hall.

Alasdair McAndrew. 2004. Introduction to Digital Image Processing withMatlab. Thomson Course Technology, USA.

Acharya, Tinku dan Ray, A.K. 2005. Image Processing. Principles andApplications. 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 PracticalApproach using CVI Tools. Prentice Hall PTR.

Rastislav Lukac dan Konstantinos. 2007. Color Image Processing. Methodsand Applications. Taylor & Francis Group, LLC

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

Bahan bacaan lain yang relevan


Lecturer:

Dr. Yeni Herdiyeni, S.Si, M.Komp

Aziz Kustiyo, S.Si, M.Komp

Grade

UTS

UAS

Tugas

Quiz

Project


Topics

Week 1 :Introduction

Week 2 : Digital Image and openCV

Week 3 :Point Processing, Color Processing

Week 4 : Image Enhancement (Spatial Filtering)

Week 5 : Image Enhancement (Histogram)

Week 6 : Restorasi Citra

Week 7 : Fourier Transformation

Mid Test


Topics #2

Week 8 : Wavelet Transformation

Week 9 : edge detection

Week 10 : Image Segmentation

Week 11 : Image Morfology

Week 12 : Image Compression RLE, HuffmanCode

Pertemuan 13 : Image Compression JPEG

Pertemuan 14 : Pattern Recognition

Final Test


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Key Stages in Digital Image Processing

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression


Key Stages in Digital Image Processing:

Image Aquisition

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression

ImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)



Image Enhancement

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression




Image Restoration

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression




Morphological Processing

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression




Segmentation

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression



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Object Recognition

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression




Representation & Description

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression




Image Compression

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression



Colour Image Processing

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression


Boundary Detection

http://www.robots.ox.ac.uk/~vdg/dynamics.html


Image Retrieval


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Boundary Detection

Finding the Corpus Callosum

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


Recognition - Shading

Lighting affects appearance




Remote Sensing - GIS


30 January 2013 241999-2007 by Richard Alan

Peters II

Image Compression

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

Original image is5244w x 4716h @

1200 ppi:

127MBytes


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Peters II

Image Compression: JPEG

JPEGqualitylevel F

ilesizeinbytes



Peters II

JPEGqualitylevel File

sizeinbytes

Image Compression: JPEG


Classification

(Funkhauser, Min, Kazhdan, Chen, Halderman, Dobkin, Jacobs)


DIP

astronomyseismology

inspection

autonomousnavigation

reconnassaince

& mappingremotesensing

surveillance

microscopy

radiology

robotic assembly digital library

ultrasonicimaging

radar,SAR

meteorology

internet

Applications of Digital Image Processing (DIP)

From Prof. Alan C. Bovik


Digital Image Processing

Digital Image Formation

Digital Image Restoration

Digital Image Enhancement

Digital Image Frequency

Image Compression

Image Segmentation

Image Recognition (case study)



Peters II

Image Formation


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Peters II

Image Formation



Peters II

Image Formation

projection

through lens

image of object



Peters II

Image Formation

projection onto

discrete sensor

array.

digital camera



Peters II

Image Formation

sensors register

average color.sampled image



Peters II

Image Formation

continuous colors,

discrete locations.discrete real-

valued image



Peters II

Digital Image Formation: Quantization

continuous color input

discretecoloroutput

continuous colors

mapped to a finite,

discrete set of colors.


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Peters II

Sampling and Quantization

pixel grid

sampledreal image quantized sampled &

quantized



Peters II

Digital Image

a grid of squares,each of which

contains a single

color

each square is

called a pixel (for

picture element)

Color images have 3 values per

pixel; monochrome images have 1

value per pixel.


original + gamma- gamma + brightness- brightness

original + contrast- contrast histogram EQhistogram mod

Pengolahan Titik



Peters II

originalblurred sharpened

Spatial Filtering



Peters II

Spatial Filtering

bandpass

filter

unsharp

masking

original



Peters II

Spatial Filtering

bandpass

filter

unsharp

masking

original

signed image with0 at middle gray


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Peters II

Motion Blurverticalregional

zoom rotational

original



Peters II

color noiseblurred image color-only blur

Noise Reduction



Peters II

5x5 Wiener filtercolor noiseblurred image

Noise Reduction



Peters II

Noise Reduction

originalperiodic

noise

frequency

tuned filter


30 January 2013 471999-2007 by Richard Alan Peters II

Color Images

Are constructed from threeintensity 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.



Peters II

Color

Images On aCRT


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Peters II

Color Processing

requires some

knowledge of how

we see colors



Peters II

Eyes Light Sensors

#(blue)


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Peters II

Color Perception

all bands luminance chrominance

red green blue

16pixelization of: Departemen Ilmu Komputer -IPBPengantar Pengolahan Citra Digital


Peters II

Color Balance

and Saturation

Uniform changes in color

components result in change of

tint.

E.g.,if all G pixel values are multiplied by

> 1then the image takes a green cast.



Peters II

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:



Peters II

The 2D Fourier Transform of a Digital Image

21 1

0 0

, , ,

ur v ciR C

R C

u v

I r c u v e

1 1 2

1

0 0

( , )

ur v cR C i

R CRC

r c

u,v I r c e

LetI(r,c) be a single-band (intensity) digital image withR

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

where

are theRx CFourier coefficients.

these complexexponentials are2D sinusoids.



Peters II

2D Sinusoids:

orientation

... are plane waves with

grayscale amplitudes, periods in

terms of lengths, ...

1sinR

cosC

2cos

2,

rcAcrI

A

= phase shift

r

c



Peters II

2D Sinusoids: ... specific orientations,and phase shifts.

r

c

r

c


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Peters II

The Value of a Fourier Coefficient

is a complexnumber with a

real part and animaginary part.

If you representthat number as amagnitude,A, anda phase, ,

..these represent the amplitudeand offset of thesinusoid withfrequency wand direction .



Peters II

The Sinusoid from the Fourier Coeff. at (u,v)



Peters II

I |F{I}| [F{I}]

The Fourier Transform of an Image

magnitude phase



Peters II

Continuous Fourier Transform

The continuous Fouriertransform assumes acontinuous image existsin a finite region of an

infinite plane.

dudvevucr vruci )(2,,I I

dcdrecrvu vruci )(2,I, I

The BoingBoing Bloggers



Peters II

Discrete Fourier Transform

The discrete Fouriertransform assumes adigital image exists on aclosed surface, a torus.

1

0

21

0

)(IC

u

R

vr

C

uciR

v

eu,vr,c

I

1

0

21

0

,I,C

c

R

rv

C

cuiR

r

ecrvu

I

The BoingBoing Bloggers



Peters II

Convolution

16,16 cr 16,16 cr

16,16 cr

Sum times 1/5

Sums of shifted and

weighted copies of

images or Fourier

transforms.


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Peters II

Convolution Property of the Fourier Transform

The Fourier Transform of a

product equals the convolution ofthe Fourier Transforms. Similarly,the Fourier Transform of aconvolution is the product of theFourier Transforms

.

bycomputedbecannconvolutiospatialaThen,

tionmultiplicapointwiserepresents

nconvolutiorepresents

.}{

Moreover,

.}{

Then,).,(and),(TransformsFourier

have),(and),(functionsLet

1GFgf

GFgf

GFgf

vuGvuF

crgcrf

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Documents

Kuliah 01 - Introduction