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

Fanio Agung Adi 2 Revan Ida Bagus 19 081223268458 Agung M 5 085717016878 Fajar S 1 Farid 8 082315847675

INTRO Gelar Budiman/GLB STTTelkom in 1997 Bachelor, Magister 2003-2005, 2006-2007 : LAPI ITB 4 months, SMART Telecom 1 year. 2008 Feb-now : POLTEK TELKOM/NTC lecturer Nov. 2008 : ITTELKOM PSD, SINSIS, PSM, Calculus, Comp System, Matdist, SISMUL, SISKOM, Microprocessor-Interface, Koding Kompresi, PBO, MobApp . gelarbudiman@telkomuniversity.ac.id;gelar08@yahoo.co.id;gelar.budiman@gmail.com 081322045924 Ketua Kelas : Sekretaris :

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

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

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%

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

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

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

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

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

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

Copyright A.Kitaoka 2003

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

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

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

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 http://www.research.att.com/~yann/ License plate readers http://en.wikipedia.org/wiki/Automatic_number_plate_recognition

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, …

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

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… “

Face recognition Who is she?

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

Login without a password… Face recognition systems now beginning to appear more widely http://www.sensiblevision.com/ Fingerprint scanners on many new laptops, other devices

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

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

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

Sports Sportvision first down line Nice explanation on www.howstuffworks.com

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

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.

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.

NASA’s Mars Spirit Rover Robotics NASA’s Mars Spirit Rover http://en.wikipedia.org/wiki/Spirit_rover http://www.robocup.org/

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

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