kuliah 01-pendahuluan.pdf

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Departemen Ilmu Komputer -IPBPengantar Pengolahan Citra Digital

Kuliah 01:

Pendahuluan

Yeni HerdiyeniDepartemen Ilmu Komputer IPB

Semester Ganjil 2008

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


Topik

• Tujuan Instruksional Umum:Mahasiswa mampu menjelaskan, mengolah danmenganalisis citra digital.

• Deskripsi:Mata kuliah ini menjelaskan karakteristik citra digital, analisis dan pengolahan citra digital seperti image formation, image restoration, image enhancement, transformasi citra dalam ruang frekuensi, kompresicitra, deteksi tepi, segmentasi citra, morfologi citra danpengenalan pola. Perangkat lunak yang digunakanMATLAB dan C

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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• Project

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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)

• Ujian Akhir Semester

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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


27 August 2008 81999-2007 by Richard Alan

Peters II

Image Formation

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Peters II

Image Formation



Peters II

Image Formation

projection through lens

image of object

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Peters II

Image Formation

projection onto discrete sensor array.

digital camera



Peters II

Image Formation

sensors register average color.

sampled image

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Peters II

Image Formation

continuous colors, discrete locations.

discrete real-valued image



Peters II

Digital Image Formation: Quantization

continuous color input

dis

cret

e co

lor

ou

tpu

t

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.

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original + gamma- gamma + brightness- brightness

original + contrast- contrast histogram EQhistogram mod

Pengolahan Titik



Peters II

originalblurred sharpened

Spatial Filtering

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Peters II

Spatial Filtering

bandpassfilter

unsharpmasking

original



Peters II

Spatial Filtering

bandpassfilter

unsharpmasking

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

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Peters II

5x5 Wiener filtercolor noiseblurred image

Noise Reduction



Peters II

Noise Reduction

originalperiodic

noisefrequency tuned filter

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27 August 2008 251999-2007 by Richard Alan Peters II

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.



Peters II

Color Images On a

CRT

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Peters II

Color Processing

requires some knowledge of how we see colors



Peters II

Eye’s Light Sensors

#(blue) << #(red) < #(green)

cone density near fovea

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Peters II

Color Sensing / Color PerceptionThese are approximations of the responses to the visible spectrum of the “red”, “green”, and “blue” receptors of a typical human eye.



Peters II

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.

Color Sensing / Color Perception

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Peters II

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inan

ceh

ue

saturatio

n

photo receptorsbrain

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



Peters II

Color Perception

all bands luminance chrominance

red green blue

16× pixelization of:

luminance and chrominance (hue+saturation) are perceived with different resolutions, as are red, green and blue.

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Peters II

Color Perception

all bands luminance chrominance

red green blue

16× pixelization of:



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 > 1 then the image takes a green cast.

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Peters II

Color Transformations

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222

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114

122

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227

236

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171

240

160

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218

222

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185

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160

171

240

103

171

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166

230

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106

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Image aging: a transformation, , that mapped:



Peters II

The 2D Fourier Transform of a Digital Image

21 1

0 0

, , ,

ur vciR C

R C

u v

I r c u v e

I

1 1 2

1

0 0

( , )

ur vcR C i

R CRC

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.

these complex exponentials are 2D sinusoids.

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Peters II

2D Sinusoids:

orientation

... are plane waves with grayscale amplitudes, periods in terms of lengths, ...

1sin

Rcos

C

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 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 .



Peters II

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

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Peters II

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

The Fourier Transform of an Image

magnitude phase



Peters II

Continuous Fourier Transform

The continuous Fourier transform assumes a continuous image exists in a finite region of an infinite plane.

dudvevucr vruci )(2,,I I

dcdrecrvu vruci )(2,I, I

The BoingBoing Bloggers

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Peters II

Discrete Fourier Transform

The discrete Fourier transform assumes a digital image exists on a closed surface, a torus.

1

0

21

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)(IC

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R

vr

C

uciR

v

eu,vr,c

I

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0

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,I,C

c

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r

ecrvu

I

The BoingBoing Bloggers



Peters II

Convolution

16,16 cr

0,0 cr

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 of the Fourier Transforms. Similarly, the Fourier Transform of a convolution is the product of the Fourier Transforms

.

bycomputed be can nconvolutio spatiala Then,

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Boundary Detection

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

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Boundary Detection

Finding the Corpus Callosum

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



Peters II

Nonlinear Processing: Binary Morphology

“L” shaped SE

O marks origin

Foreground: white pixels

Background: black pixels

Cross-hatched pixels are indeterminate.

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Peters II

Image Compression

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

Original image is 5244w x 4716h @ 1200 ppi: 127MBytes



Peters II

Image Compression: JPEG

JPEG

qu

alit

y le

vel File size in

bytes

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Peters II

JPEG

qu

alit

y le

vel File size in

bytes

Image Compression: JPEG


Recognition - Shading

Lighting affects appearance

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Classification

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

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