Citra-2_ Introduction and Applications

8/8/2019 Citra-2_ Introduction and Applications

http://slidepdf.com/reader/full/citra-2-introduction-and-applications 1/54

Digital Image ProcessingSyllabus & Grading System, and Introduction

Semester Gasal 2010/2011

Prof.Dr. Aniati Murni Arymurthy (Bldg. A, R 1202)

Faculty of Computer Science

University of Indonesia



2

Grading System, Text Book, Programming

Grading System:

• Homework and Programming Assignments (~30%)

• Mid Test / UTS (~35%) Final Exam / UAS (~35%)References:

• Slides & Hand outs

• Gonzalez & Woods, 2002/2008, Digital Image Processing

Programming:

• C++ or Java or MATLAB



3

Teaching and Learning Methods

• Lectures (target of course content) – 28 sessions

• Independent Study (critical thinking) - 1 topic

• Presentation (communication ability – small class)

• Paper/report writing (knowledge composition)

• Team work (project management)• Programming (ability to implement methods) – 2 assignments

• Mid Test and Final Test (comprehension test)



4

University / Faculty Regulations

Minimum attendance in class : 75%

No free rider in working group

No plagiarism in report / academic work andwriting

No cheating in examination

No UTS/UAS susulan (without appropriatereason)



Three Areas of Study that Related toImage and Picture Processing

5



Three Areas of Study that Related toImage and Picture Processing

Computer Graphics

Image Processing

Pattern Recognition / Computer Vision / Artificial Intelligence

6



Description

Pavlidis, 1986

Image

1950 Image Processing

1970 Computer

Graphics

1970 Computer Vision

1960 Pattern Recognition

Artificial Intelligence



Michigan State University, 1990

Description Generated Image



Computer Graphics (1)

Object Description Generated Image

(Murni, 1979)



Computer Graphics (2)

Wire Frame Drawing Realism Drawing(Hearn and Baker, 1986)



Computer Graphics

A process, technique, and method to generate a picture based on the description of both its objects and backgrounds;

A process, technique, and method to

create a realism effect on the objects and backgrounds contained in a picture;

Drawing a picture and animating objects using a computer.

11



Image Processing (1)

(JPL, 1972)

Input/Degraded Image Output/Enhanced Image



Image Processing andPattern Recognition (2)

CASI image of Bogor area Classified / Thematic Image

(Source: The Map Indonesia) (Source: Wiweka, 2006)



Digital Image Processing

Image quality enhancement: (a) radiometric aspect (contrast enhancement, colour transformation, image restoration); and (b) geometric aspect (rotation,translation, scale, geometric transformation);

Feature/image extraction and selection to obtain images that would be optimal for analysis purpose;

Data reduction and image compression (for the

purpose of efficiency in data storage, data transmission, and data processing time);

Information extraction, object recognition and

description that is contained in an image.

14



15

Computer Vision (1)

Garage Bushes Grass House Sky Tree1 Tree2

Roof Side Roof Side1 Side2

(Ballard, 1992)

Input Images Descriptions



Computer Vision (2)

16

Bahasa isyarat lainnya: menggunakan bahasa tangan dan ada juga yang disebut sebagai ‘body language’; mengangguk (jarak

antara garis alis dan mulut mengecil), menggeleng (jarak antaragaris mata kiri dan kanan mengecil).



17

Computer Vision

Pattern Recognition : Segmentation and Classification, Speaker Recognition – siapakah yangmembacakan kalimat tersebut?;

Computer Vision : Object Recognition and Description (Object Structure), Word and Vowel Recognition – kata-kata apa saja yang membentuk

kalimat tersebut; Art ificial Intelligence : What is illustrated by this

image?, Speech Understanding – apakah arti darikalimat tersebut?.



Assignment-1 (Tugas Kelompok)

Bentuk kelompok yang masing-masing terdiri dari 3mahasiswa.

Untuk setiap kelompok, cari 3 papers yang masing-masing masuk kategori: (i) computer graphics ; (ii) digital image processing; dan (iii) pattern recognition ataucomputer vision atau artificial intelligence. Lebih baik lagi bila dari problem domain yang sama.

Setiap mahasiswa dalam setiap kelompok membahas 1paper yang dipilih kelompok.

Buat laporan sesuai dengan format laporan (lihat

halaman berikut).

Laporan diserahkan pada tanggal 5 Oktober 2010. 18



Report LayOut

Tuliskan identifikasi kelompok dan anggota-anggotanya.

Pengantar: jelaskan apakah ada kaitannya atau tidak

antara 3 papers yang dibahas dalam kelompok. Secara berurutan (paper 1 s/d paper 3), tuliskan:

Judul paper dan identifikasi pembahas

Jelaskan mengapa paper tersebut termasuk padakategori yang dimaksud, serta jelaskan inti dari isipaper (misal: input, proses, output).

Jumlah halaman laporan adalah 3 – 5 halaman.

19



20

Digital Image Processing: Applications

Orthodontic and Dentistry (Kedokteran Gigi);

Biomedical (Kedokteran);

Remote Sensing (Penginderaan Jarak Jauh); Industry;

Gesture Language (Bahasa Isyarat); Character Recognition (Pengenalan Karakter);

Digital Signature & Biometric Data (for e-Commerce,

Banking etc.).



Research Activities related toLab for Pattern Recognition, IP, and CBIRS

Fasilkom Multi-Lab Research: Mobile-based Batik Content-

Based Image Retrieval System (2009) Sponsored by UI: Change Detection Based on Multidate

Remote Sensing Data (2009)

Sponsored by UI: Breast Cancer Image Detection Based onLab and Mammogram Data (2009)

Sponsored by UI: ICT based Health Services (2010)

Sponsored by DepDikNas: Content-Based Image RetrievalSystem (2008-2010)

Sponsored by MenRisTek: Cultural Heritage ArtefactInventory System (2010)

Sponsored by UI (being reviewed): ECG for Handling SleepDisorder (2010)



22

Orthodontic Application (1)(Source: Budhiantini Bagyo, S2 Thesis, UI, 1993)



Image Registration: Citra BiomedisSource: J. Kusnoto and A. Murni, 2007

Pasangan Titik Kontrol



24

Orthodontic Application (2a)(Sumber: Joko Kusnoto et al ., AICBET-2007)

Cephalo Image Indonesian Deuteromalay

(hanya contoh, Normative facial profile Preferred facial profile

bukan data pemilik foto). Display simulasi facial profile

Cephalo data untuk dengan metrik normative dan preferred.

menentukan nilai metrik. Untuk menampilkan simulasi hasiltreatment yang akan diperoleh.



25

Orthodontic Application (2b)

Orthodontic treatment is usually done basedon normative measures (normal averagemeasures)

As an alternative, before treatment the

patient could have an opportunity to chooseher own preference measures based onsimulated preference face profiles

The treatment is then done based on thepatient’s preference measures instead of thenormative measures

Further works: 3-D profiles



26

Dentistry Application (1a)(Source: Tirza, S1 Technical Report, 2004)

X-Y Axis of The Forehead Sample points from (x0,y0) to (x31,y31)(Source of Image: M. Inoue, 1990)

Bregma

Nasion



27

Dentistry Application (1b)(Source: Tirza, S1 Technical Report, 2004)

A curve from Nasion landmark (lekukan dahi ke hidung)to Bergman landmark (ubun-ubun) is extracted from acephalometric image;

Coefficients of Fourier transform and wavelet transform

are computed for the curve; A discriminant analysis is used to determine the gender

type of the person recorded in the cephalometric image

based on the Nasion and Bergman curve and Fourier /wavelet analysis using a number of man and womantraining sample data;

The experimental results show that the use of wavelet

transform gives better recognition accuracies comparedto the use of Fourier transform.



Bi di l A li ti (2 )



29

Biomedical Application (2a)Diagnosing a Pap Smear Cell Image

(Source: Farida and Addiati , 2007; Amalia and Phyllisia, 2008;

and E.P. Giri and A. Murni, 2008)

Single pap smear cell image Seven condition categories

Nucleus

Cytoplasm 20 Features Classification Method

Background.

(J. Indarti, FKUI) (T. Farida, Fasilkom UI) (Jantzen et al ., TUD)



Diagnosing a Pap Smear Cell Image Based onCBIR (2b)

Diagnosed Image Database

(Source of images:

Jantzen et al ., TUD)

Query By ExampleImage to be

diagnosed

Searching

and

Matching

carcinoma

carcinoma

Retrieved Similar Images

severe dysplasia

moderate dysplasia

mild dysplasia

treatment



ECG Analysis & Sleep Disorder

Device design Deep sleep detection

Polsomnography and ECG



ECG and Arrythmia(Sumber: Asep Insani, 2010)

Normal

Bradycardia

Tachycardia

Sudden Cardiac Death

ECG Signal Fourier Transform Spectrum



33

Remote Sensing Application (1)(Source: A. Murni, 1997)

From Left to Right

Top to Bottom:Optical-Sensor Image;Classified Optical Image;Joint Prob. Based Fusion;SAR-Sensor Image;

Classified SAR Image;High Rank based Fusion.

(Source of original /unprocessed images:

BAKOSURTANAL RI)



34

Remote Sensing Application (2)(Source: A. Murni, 1997)

From Left to RightTop to Bottom:

Optical-Sensor Image;Classified Optical Image;Mosaic Image;SAR-Sensor Image;Classified SAR Image;Fused Image.

(Source of original /unprocessed images:BAKOSURTANAL RI)



35

Remote Sensing Application (3)(Source: Rohmah and Murni, 1997 and 2001)

From Left to RightTop to Bottom:

Optical-Sensor Image;Classified Optical Image;Cloud free optical image;SAR-Sensor Image;Classified SAR Image;Cloud-free classified image.

(Source of original /unprocessed images:BAKOSURTANAL RI)





37

Automatic Image Registration(Source: Skripsi S1 Gunawan, 2006)

Reference Image

Sensed Image

Registered Image

(Source of image http:/ / imagers.gsfc.nasa.gov)



38

Research Design (2006-2010)

Content-Based Image Retrieval System

(Status 2008)



Query By Example to CBIR System(Source of raw images: http://earth.google.com/

CBIR system: Eka Aditya, Fasilkom UI, 2006)

Example of Vegetation Area

the least relevant

The 1st Indonesian Geospatial Technology Exhibition, Jakarta, August 23-27, 2006.

http://earth.google.com/




Query By Composition of Objects(Source of raw images: http://earth.google.com/

CBIR system: Eka Aditya, Fasilkom UI, 2006)

The 1st Indonesian Geospatial Technology Exhibition, Jakarta, August 23-27, 2006.

30% Water; 30% Vegetation; and 40% Buildings.





41

Query By Example(Mostly Tree)(Source: S.H. Wijono, 2008)

(Source of raw images :http://earth.google.com and

PT. The Map Indonesia Data)

Different scale of image canalso be retrieved.





42

Aplication in Industry (1)(Source: Castleman, 1972)

Diameter

Redness

Fruit Sorter

Cherries Apples

Lemons Jackfruits

Feature Space Diagram (2 features)



43

Aplication in Industry (2)(Source: Jain dan Murni, 1990)

Original Image Edge Image Recognized Circle

(MSU, 1990) (A. Murni, 1990)



44

Aplication in Industry (3)(Source: Jain, 1990)

Types of Object Feature Space Diagram



45

Applications in Gesture Language(Source: MSU, 1990)

Bahasa isyarat lainnya: menggunakan bahasa tangan dan ada juga yang disebut sebagai ‘body language’; mengangguk (jarak

antara garis alis dan mulut mengecil), menggeleng (jarak antaragaris mata kiri dan kanan mengecil).





47

Applications in Character Recognition (2)(Source: Edi, 2002)

Huruf hasil scanning Huruf setelah ‘skeletonizing’



48

Applications in Character Recognition (3)(Source: Skripsi S1 Juanita Rohali, 1991)

BAP AK BER UANG YANG J AH AT

PAD A ZAMAN DAHU L U KALA ADA SE E KOR BER UANG YANG NAKAL DA N JA HAT

BER UA NG I TU BE RN AMA XAM I N

DIA S UKA ME M AKAN AN AK AN AK YANG TI DAK D

I SU KAI OLEH I BUN YA SEHINGG A LA M A KELAMAAN ANAK AN AK DI DUNIA ME NJADI MU SNAH B I NASA KAR EN A

DI MAKAN OLEH BAPAK BE RUAN G TERS EBUT



49

Human Biometrics & Features(Source: Kompanets et al .; Uludag, 2000; Skripsi S1 Maukar, 1991)

Citra Wajah Citra Sidik Jari



Old Document Image Restoration(Sumber: Hilda Deborah, 2010)

Need a further work in image preprocessing and postprocessing



Realism vs Abstractionism in Paintings(Sumber: Tieta Antaresti, 2010)

Correctly Recognized as Abstractionism painting (Left) andRealism painting (Right)

Incorrectly Recognized as

Abstractionism painting (Left) andRealism painting (Right)

Average recognition accuracy is66.23%







The End

Date post:	10-Apr-2018
Category:	Documents
Upload:	nijaad
View:	220 times
Download:	0 times

Citra-2_ Introduction and Applications

Documents