1. DSP Laboratory – TELKOM UNIVERSITY
1 COMPUTER VISION
DSP Laboratory – TELKOM UNIVERSITY

2. Fanio
Agung Adi 2
Revan
Ida Bagus
Agung M
Fajar S 1
Farid

3. INTRO Gelar Budiman/GLB
STTTelkom in 1997 Bachelor, Magister ,
: LAPI ITB 4 months, SMART Telecom 1 year.
2008 Feb-now : POLTEK TELKOM/NTC lecturer
Nov : ITTELKOM
PSD, SINSIS, PSM, Calculus, Comp System, Matdist, SISMUL, SISKOM, Microprocessor-Interface, Koding Kompresi, PBO, MobApp .
Ketua Kelas : Sekretaris :

4. CLASS RULE
Percentage of this object : Mid test and Post test 60% Quiz 10% + Project 20%+ Task 10%
Late > 10 min | > my arrival, per 10 minutes bonus -1, to remove -1 bonus,-1=Rp 5000, >20 min lecturer doesn’t come into the class  poin 2 for all students who are not absence.
Present : min. 75%, it’s out of practicum, it’s out of sickness in the hospital. Absence is online, complaint only can be done max 1 week after lecture.
Not allow to add manual presence if name does not exist yet, please confirm roster/BAA.
Advice Culture (5-7 menit)
Whiteboard must be clean before having lecture.
Integrity : Both of the “bad” students : E.
Copying/Cheating
Titip Absen

5. Perhitungan Nilai
Percentage of this object : Mid test and Post test 60% Quiz 10% + Project 20%+ Task 10%
A >= 80
0.6xU+0.1xQ+0.2xP+0.2xT=NA
Jika mhs rajin : 0.1xQ+0.2xP+0.1xT=35
0.6xU=80-35=45
U=75

6. Kehadiran Jumlah pertemuan = 14x2=28 pertemuan 80%x28=22.4  23
0-2x  A
1x-4x  B
3x-6x  C
4x-6x  D/E
Remedial >= 55%

7. Project > what I desired  tdk ikut UAS (A)
== what I desired  project A tapi ikut UAS, B jika tdk ikut UAS
< what I desired  project B, dan hrs ikut UAS

8. 1. Multimedia Multi : Banyak Media Telepon, televisi, news-paper,
floppy disc, CD-R, DVD
Multi : Banyak
Media
Teks, Grafis,
Suara, Video

9. What is computer vision?
Clips: terminator 2, enemy of the state (from UCSD “Fact or Fiction” DVD)
Terminator 2

10. Every picture tells a story
Goal of computer vision is to write computer programs that can interpret images

11. Can computers match (or beat) human vision?
If you can write a formula for it, computers can excel
Computer vision can’t solve the whole problem (yet), so breaks it down into pieces.
Many of the pieces have important applications.
Yes and no (but mostly no!)
humans are much better at “hard” things
computers can be better at “easy” things

12. Human perception has its shortcomings…
Example where humans make mistakes that computers can avoid
Sinha and Poggio, Nature, 1996

13. Copyright A.Kitaoka 2003

14. Current state of the art
The next slides show some examples of what current vision systems can do

15. Earth viewers (3D modeling)
Image from Microsoft’s Virtual Earth
(see also: Google Earth)

16. Photosynth http://labs.live.com/photosynth/
Based on Photo Tourism technology developed here in CSE! by Noah Snavely, Steve Seitz, and Rick Szeliski

17. Optical character recognition (OCR)
Technology to convert scanned docs to text
If you have a scanner, it probably came with OCR software
Digit recognition, AT&T labs
License plate readers

18. Face detection Many new digital cameras now detect faces
Why would this be useful? Main reason is focus. Also enables “smart” cropping.
Many new digital cameras now detect faces
Canon, Sony, Fuji, …

19. Smile detection?
Sony Cyber-shot® T70 Digital Still Camera

20. Object recognition (in supermarkets)
LaneHawk by EvolutionRobotics
“A smart camera is flush-mounted in the checkout lane, continuously watching for items. When an item is detected and recognized, the cashier verifies the quantity of items that were found under the basket, and continues to close the transaction. The item can remain under the basket, and with LaneHawk,you are assured to get paid for it… “

21. Face recognition
Who is she?

22. Vision-based biometrics
“How the Afghan Girl was Identified by Her Iris Patterns” Read the story

23. Login without a password…
Face recognition systems now beginning to appear more widely
Fingerprint scanners on many new laptops, other devices

24. Object recognition (in mobile phones)
This is becoming real:
Microsoft Research
Point & Find, Nokia

25. Special effects: shape capture
The Matrix movies, ESC Entertainment, XYZRGB, NRC

26. Special effects: motion capture
Pirates of the Carribean, Industrial Light and Magic
Click here for interactive demo

27. Sports Sportvision first down line
Nice explanation on

28. Slide content courtesy of Amnon Shashua
Smart cars
Slide content courtesy of Amnon Shashua
Mobileye
Vision systems currently in high-end BMW, GM, Volvo models
By 2010: 70% of car manufacturers.
Video demo

29. Vision-based interaction (and games)
Digimask: put your face on a 3D avatar.
Nintendo Wii has camera-based IR tracking built in. See Lee’s work at CMU on clever tricks on using it to create a multi-touch display!
“Game turns moviegoers into Human Joysticks”, CNET
Camera tracking a crowd, based on this work.

30. Vision in space Vision systems (JPL) used for several tasks
NASA'S Mars Exploration Rover Spirit captured this westward view from atop a low plateau where Spirit spent the closing months of 2007.
Vision systems (JPL) used for several tasks
Panorama stitching
3D terrain modeling
Obstacle detection, position tracking
For more, read “Computer Vision on Mars” by Matthies et al.

31. NASA’s Mars Spirit Rover
Robotics
NASA’s Mars Spirit Rover

32. Medical imaging Image guided surgery 3D imaging Grimson et al., MIT
MRI, CT

33. Materi Overview Citra Operasi Dasar Citra
Histogram dan Domain Frekuensi Citra
Filtering Citra
Operasi Morfologi Citra
Ekstraksi Ciri Citra
Klasifikasi Dan Deteksi
Kompresi JPEG