Permasalahan Image Processing : Capture, Modelling, Feature Extraction,image Segmentation - Repository UNIKOM

  Pertemuan-2:

  

IMAGE

PROCESSING KK-Komputasi dan Kecerdasan Buatan Teknik Komputer Universitas Komputer Indonesia-UNIKOM

  John Adler Bahan kuliah pertemuan-2

Permasalahan image processing : Capture

• modelling, feature extraction,image

  segmentation Sejarah Digital Image Processing (DIP)
• Beberapa contoh
• Key Stages in Digital Image Processing •

  Permasalahan image processing : EXTRACTION, IMAGE SEGMENTATION

Permasalahan capture

  

Capture merupakan proses awal dari image

• processing untuk mendapatkan gambar Proses capture membutuhkan alat-alat

• capture yang baik seperti kamera, scanner,

  light-pen dan lainnya, agar diperoleh gambar yang baik.

  Gambar yang baik akan banyak membantu

• dalam proses selanjutnya.

Permasalahan Modeling Dalam modeling diperlukan analisa matematika yang cukup rumit, khususnya pemakaian kalkulus dan transformasi geometri.

  (inilah sebabnya di semua jurusan fakultas teknik, kuliah matematika menjadi sangat penting !!)

Permasalahan Feature Extraction

  Setiap gambar mempunyai karakteristik tersendiri,

• sehingga fitur tidak dapat bersifat general tetapi sangat tergantung pada model dan obyek gambar yang digunakan. Fitur dasar yang bisa diambil adalah warna, bentuk dan
• tekstur. Fitur yang lebih kompleks menggunakan segmentasi, clustering dan motion estimation Pemakaian statistik dan probabilitas, pengolahan sinyal
• sampai pada machine learning diperlukan di sini

Permasalahan Image Segmentation

  Bagaimana memisahkan obyek gambar

• dengan backgroundnya

  Bagaimana memisahkan setiap obyek

• gambar Teknik clustering apa yang sesuai
• dengan model dan obyek gambar yang digunakan

  Contoh Postal Code Problem

  History of Digital Image Processing Early 1920s: One of the first applications of

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  digital imaging was in the news-

  g in ss ce

  paper industry

  ro P e

• – ag

  The Bartlane cable picture

  m

  I al it

  transmission service

  ig , D

  Early digital image

• – ds oo

  

Images were transferred by submarine cable

  W & z

  between London and New York

  le za

• – on

  Pictures were coded for cable transfer and

  G om fr

  reconstructed at the receiving end on a telegraph

  n ke ta es printer ag Im

  History of DIP (cont…) Mid to late 1920s: Improvements to the

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  Bartlane system resulted in higher quality

  g in ss ce

  images

  ro P e ag

• – m

  New reproduction

  I al it

  processes based

  ig , D ds

  on photographic

  oo W &

  techniques

  z le

• – za on

  Increased number

  G om fr

  of tones in

  n ke

  Improved

  ta

  reproduced images Early 15 tone digital

  es

  digital

  ag Im

  image History of DIP (cont…) 1960s: Improvements in computing technology

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  and the onset of the space race led to a surge

  g in ss ce

  of work in digital image processing

  ro P e

• – ag m

  1964: Computers used to

  I al it

  improve the quality of

  ig , D ds

  images of the moon taken

  oo W &

  by the Ranger 7 probe

  z le

• – za on

  Such techniques were used

  G om fr

  in other space missions

  n ke ta

  including the Apollo landings

  es

A picture of the moon

  ag Im

  History of DIP (cont…)

1970s: Digital image processing begins to be

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  used in medical applications

  g in

  1979: Sir Godfrey N.

• – ss ce ro P e ag Hounsfield & Prof. Allan M.

  m

  I al it

  Cormack share the Nobel

  ig , D ds

  Prize in medicine for the

  oo W & invention of tomography, z le za

  the technology behind

  on G om Computerised Axial fr n

  Typical head slice CAT

  ke

  Tomography (CAT) scans

  ta es

  image

  ag Im

  History of DIP (cont…)

• 1980s - Today: The use of digital image

  processing techniques has exploded and they

are now used for all kinds of tasks in all kinds of

areas

• – Image enhancement/restoration
• – Artistic effects
• – Medical visualisation
• – Industrial inspection
• – Law enforcement
• – Human computer interfaces

  

Beberapa Bidang Ilmu yang Berhubungan

dengan Image

  Computer Graphics : Kreasi image

Image processing : penyempurnaan

atau manipulasi gambar- yang hasilnya gambar lain

  Computer vision : analisis isi image Pengolahan Data Berdasarkan Input/Output

  IMAGE Image Processin g

  Computer Vision

DESKRIP SI

  Computer

Graphics

Data Mining, dll

IN P U T

  Dua Macam Aplikasi IP Meningkatkan informasi bergambar untuk interpretasi manusia Pemprosesan data image untuk menyimpan, mentransmisikan, dan merepresentasikan untuk persepsi mesin otonomi

Bidang yang Memanfaatkan IP

  Berdasarkan sumber dari image: Radiasi dari spektrum elektromagnetik Akustik Ultrasonik Elektronik (dalam bentuk sinar elektron yang digunakan dalam mikroskop elektron) Komputer (image sintetis yang digunakan untuk pemodelan dan visualisasi) Persoalan di dalam IP

• Capture • Modeling • Feature Extraction • Image Segmentation

Permasalahan Capture

  Capture (menangkap gambar) merupakan

• proses awal dari image processing untuk mendapatkan gambar Proses capture membutuhkan alat-alat

• capture yang baik seperti kamera, scanner,

  light-pen dan lainnya, agar diperoleh gambar yang baik.

  Gambar yang baik akan banyak membantu

• dalam proses selanjutnya.

Alat-alat Capture Sesuai Frekuensinya

Hasil Capture : Gamma-Ray Imaging

  Nuclear Image :

  a. Bone scan

  b. PET (Positron Emission Tomography) image.

  Astronomical Observations

  c. Cygnus Loop Nuclear Reaction d. Radiasi Gamma Hasil Capture : X-Ray Imaging

  Hasil Capture : Ultraviolet Imaging

  Hasil Capture : Visible Imaging

  Hasil Capture : Infrared Imaging

  Hasil Capture : Imaging in Microwave Band

  Imaging radar : satu- satunya cara untuk menjelajahi daerah yang tidak dapat diakses dari permukaan bumi Radar image dari pegunungan di tenggara Tibet

  perhatikan kejelasan dan detail gambar, tidak terhalang oleh awan atau Hasil Capture : Imaging in Microwave Band

  Aplikasi ilmu Geologi : eksplorasi mineral dan minyak menggunakan suara dalam spektrum suara rendah (ratusan Hz)

  

Model seismik dari image cross-sectional

Gambar panah menunjukkan perangkap

  

(bright spots) hidrokarbon (minyak dan

atau gas)

Hasil Capture : Ultrasound Imaging

  Peralatan medis :

  a. Bayi

  b. Melihat bayi dari sisi yang lain c. Thyroids

  d. Lapisan tulang menggambarkan lesion Hasil Capture : Imaging in Radio Band

Menggenerate image oleh komputer

  Fraktal : an iterative

• •

  reproduction of basic pattern according to some mathematical rules (a) dan (b) Pemodelan komputer

• •

  3D (c) dan (d)

  Examples: Image Enhancement One of the most common uses of DIP

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  techniques: improve quality, remove noise etc

  g in ss ce ro P e ag m I al it ig , D ds oo W & z le za on G om fr n ke ta es ag Im

Examples: The Hubble Telescope

  Launched in 1990 the Hubble

• telescope can take images of very distant objects However, an incorrect mirror
• made many of Hubble’s images useless Image processing
• techniques were used to fix this

  Examples: Artistic Effects

• used to make images more visually appealing, to add special effects and to make composite images

Examples: Medicine

Take slice from MRI scan of canine heart, and

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  find boundaries between types of tissue

  g in

• – ss ce

  Image with gray levels representing tissue

  ro P e ag

  density

  m

  I

• – al it ig Use a suitable filter to highlight edges , D ds oo W & z le za on G om fr n ke ta es ag Im

Examples: GIS Geographic Information Systems

• 00
• – (2 g in ss

  Digital image processing techniques are used

  extensively to manipulate satellite imagery

  ce ro P e ag

• – m

  Terrain classification

  I al

• – it ig Meteorology , D ds oo W & z le za on G om fr n ke ta es ag Im

Examples: GIS (cont…)

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  the World data set

  in ss

• – ce ro P Global inventory of e ag m

  human settlement

  I al it ig

• – , D ds oo

  Not hard to imagine

  the kind of analysis

  W & z le

  that might be done

  za on G

  using this data

  om fr n ke ta es ag Im

  Examples: Industrial Inspection

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  expensive, slow and

  in ss ce ro P unreliable e ag

  I al Make machines do the it ig , D

• m

  job instead

  ds oo

• W & z le za on

  Industrial vision

  systems

  G om fr

  are used in all kinds of

  n ke ta es

  industries

  ag Im

  Examples: PCB Inspection

• Machine inspection is used to determine that all

Printed Circuit Board (PCB) inspection

• – components are present and that all solder joints are acceptable Both conventional imaging and x-ray imaging are
• – used

Examples: Law Enforcement

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  techniques are used

  in ss ce ro P extensively by law e ag m

  I

  enforcers

  al it ig

• – , D ds oo

  Number plate

  recognition for speed

  W & z le

  cameras/automated

  za on G

  toll systems

  om fr

• – n ke Fingerprint recognition ta es ag
• – Im

  Enhancement of CCTV Examples: HCI

• computer interfaces more natural

  Face recognition

• – Gesture recognition
• – Does anyone remember the
• user interface from “Minority Report”? These tasks can be

  Key Stages in Digital Image Processing

Key Stages in Digital Image Processing

  Image Acquisition Image Restoration

  Morphological Processing Segmentation

  Representation & Description Image Enhancement

  Object Recognition Problem Domain Colour Image

  Image

  (2 g in ss

  Key Stages in DIP:Image Aquisition ce ro P e ag m l I

  Image Morphological ita

  

Restoration Processing

ig , D ds oo

  Image Segmentation

  W Enhancement

  & z le za

  Image Object on

  Acquisition Recognition

  G om fr n

  Representation ke

  Problem Domain & Description ta es

  Colour Image Image

  (2 g in ss

  Key Stages in DIP:Image Enhancement ce ro P e ag m l I

  Image Morphological ita

  

Restoration Processing

ig , D ds oo

  Image Segmentation

  W Enhancement

  & z le za

  Image Object on

  Acquisition Recognition

  G om fr n

  Representation ke

  Problem Domain & Description ta es

  Colour Image Image

  (2 g in ss

  Key Stages in DIP: Image Restoration ce ro P e ag m l I

  Image Morphological ita

  

Restoration Processing

ig , D ds oo

  Image Segmentation

  W Enhancement

  & z le za

  Image Object on

  Acquisition Recognition

  G om fr n

  Representation ke

  Problem Domain & Description ta es

  Colour Image Image

  (2 g in

  Key Stages in Digital Image Processing: ss ce

  Morphological Processing ro P e ag m l I

  Image Morphological ita

  

Restoration Processing

ig , D ds oo

  Image Segmentation

  W Enhancement

  & z le za

  Image Object on

  Acquisition Recognition

  G om fr n

  Representation ke

  Problem Domain & Description ta es

  Colour Image Image

  (2 g in ss

  Key Stages in DIP: Segmentation ce ro P e ag m l I

  Image Morphological ita

  

Restoration Processing

ig , D ds oo

  Image Segmentation

  W Enhancement

  & z le za

  Image Object on

  Acquisition Recognition

  G om fr n

  Representation ke

  Problem Domain & Description ta es

  Colour Image Image Key Stages in DIP: Object Recognition

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Restoration Processing

  in ss ce ro P e ag m

  I Image al

  Segmentation

  it

Enhancement

  ig , D ds oo W &

  Image Object

  z le

  Acquisition Recognition

  za on G om fr n

  Representation

  ke

  Problem Domain

  ta

  & Description

  es ag Im

  Colour Image Image

  (2 g in

  Key Stages in Digital Image Processing: ss ce

  Representation & Description ro P e ag m l I

  Image Morphological ita

  

Restoration Processing

ig , D ds oo

  Image Segmentation

  W Enhancement

  & z le za

  Image Object on

  Acquisition Recognition

  G om fr n

  Representation ke

  Problem Domain & Description ta es

  Colour Image Image Key Stages in Digital Image Processing:

Image Compression

  Image Morphological

Restoration Processing

Image

  Segmentation Enhancement Image

  Object Acquisition Recognition Representation

  Problem Domain & Description Colour Image Image Key Stages in Digital Image Processing:

Colour Image Processing

  Image Morphological

Restoration Processing

Image

  Segmentation Enhancement Image

  Object Acquisition Recognition Representation

  Problem Domain & Description Colour Image Image

Penutup

  Ada beberapa hal yang harus dikuasai sebelum menguasai

materi di dalam image processing

yaitu : matematika, aljabar, pengolahan sinyal, matriks dan transformasi linier, statistik,

Referensi slide

  Brian Mac Namee, Digital Image Processing :

• Introductio Nana Ramadijanti, Image Processing : Day-1,
• Laboratorium Computer Vision, PENS-ITS, Surabaya Achmad Basuki, Pengantar Pengolahan •

  Citra, Laboratorium Computer Vision, PENS-

  ITS, Surabaya

Image Processing Mempelajari Apa?

  

• http://www.mathworks.com/products/
• image/description4.html

Image Processing Mempelajari Apa? (Contd.)

• http://www.mathworks.com/products/image/
• description7.html

  INTRODUCTION In electrical engineering and computer science image

processing is any form of signal processing for which the input

is an image, such as a photograph or video frame; the output of image processing may be either an image or, a set of

characteristics or parameters related to the image. Most image-

processing techniques involve treating the image as a two- dimensional signal and applying standard signal-processing techniques to it.

  

Image processing usually refers to digital image processing, but

optical and analog image processing also are possible. This article is about general techniques that apply to all of them. The acquisition of images (producing the input image in the first place) is referred to as imaging.

  Definitions of Image Processing •The general term "image processing" refers to a computer discipline wherein digital images are the main data object. This type of processing can be broken down into several sub-categories, including: compression, image enhancement,

image filtering, image distortion, image display

and coloring.

•Any activity that transforms an input image into

an output image. Manipulation of an image to improve or change some quality of the image

This Process involves two aspects

  

Improving the visual appearance of images to

a human viewer.

  Preparing images for measurement of the features and structures present.

Why do we need it……?

  Since the digital image is invisible it must

• prepare for viewing one or more output device. The digital image can be optimized for the application by enhancing or altering the appearance of the structures within it.

  It might be possible to analyze the image in

• the computer and provide clues to the radiologist to help direct important/suspicious structure.

Acquiring Images

  Since the digital image is

• invisible it must prepare for viewing one or more output

  device. The digital image can

  be optimized for the application by enhancing or altering the

  appearance of the structures

  within it. It might be possible to analyze
• the image in the computer and provide clues to the radiologist to help direct important/

High Resolution

  

The process of obtaining a high resolution (HR) image or a

sequence of HR from a set of low resolution (LR) observation. HR technique has applied to a variety of fields such as obtaining.

  Improve still images. High definition television.

High performance color liquid crystal display (LCD) screen.

Video surveillance. Remote sensing and Medical imaging.

Color Spaces

  Conversion from RGB (The brightness of

• individual red, green and green signal at defined wavelength) to YIQ/YUV and to other color encoding schemes is straightforward and losses no information.

Image Sensors

  Digital processing requires images to be

• obtained in the form of electrical signals. These signals can be digitized into sequence of numbers which can be processed by a computer.

  Image Intensity Equalization using Histograms

  

Image intensity Equalization is the process of converting the given image into the

desired manner using Histogram. In histogram equalization we are trying to maximize the image contrast by applying a gray level transform which tries to flatten the resulting histogram. The gray level transform is a scaled version of the original image's cumulative histogram. That is, the gray level transform T is given by T[i] = (G-1)c(i), where G is the number of gray levels and c(i) is the normalized cumulative histogram of the original image.

  When we want to specify a non-flat resulting histogram, we can use the following steps: Specify the desired histogram g(z) Obtain the transform which would equalize the specified histogram, Tg, and its inverse Tg

• 1

  Get the transform which would histogram equalize the original image, s=T[i] Apply the inverse transform Tg

• 1
• 1

  on the equalized image, that is z=Tg

  [s] Input Image corresponding histogram Output Image corresponding histogram

  Multiple Images: It may constitute a series of views of the same area using different wavelength of light and other signals. Examples

includes the image processed by satellites those images may

require processing. Hardware Requirement: A general purpose computer can be used for image processing; four key demands must be met

• high resolution image display • Sufficient memory transfer bandwidth.
• Sufficient storage space

Software Requirements: Adobe Photoshop, Corel draw, Serif photo plus Mat lab etc. CONCLUSION:

Adobe The Image processing is used to get/acquire

image in a desired manner by using some of the software’s

shown above without affecting its original input image and

their may not be loos of data during the conversion process.