1. CSE 803: Computer Vision Naveed Sarfraz Khattak

2. What is Computer Vision?

3. Computer vision is the science and technology of machines that see. Concerned with the theory for building artificial systems that obtain information from images. The image data can take many forms, such as a video sequence, depth images, views from multiple cameras, or multi-dimensional data from a medical scanner

4. Computer Vision Make computers understand images and videos. What kind of scene? Where are the cars? How far is the building? …

5. Components of a computer vision system Lighting Scene Camera Computer Scene Interpretation Srinivasa Narasimhan’s slide

6. Computer vision vs human vision What we seeWhat a computer sees

7. Vision is really hard Vision is an amazing feat of natural intelligence – Visual cortex occupies about 50% of Macaque brain – More human brain devoted to vision than anything else Is that a queen or a bishop?

8. Vision is multidisciplinary From wiki Computer Graphic s HCI

9. Why computer vision matters Safety HealthSecurity Comfort Access Fun

10. A little story about Computer Vision In 1966, Marvin Minsky at MIT asked his undergraduate student Gerald Jay Sussman to “spend the summer linking a camera to a computer and getting the computer to describe what it saw”. We now know that the problem is slightly more difficult than that. (Szeliski 2009, Computer Vision)

11. A little story about Computer Vision In 1966, Marvin Minsky at MIT asked his undergraduate student Gerald Jay Sussman to “spend the summer linking a camera to a computer and getting the computer to describe what it saw”. We now know that the problem is slightly more difficult than that. Founder, MIT AI project

12. A little story about Computer Vision In 1966, Marvin Minsky at MIT asked his undergraduate student Gerald Jay Sussman to “spend the summer linking a camera to a computer and getting the computer to describe what it saw”. We now know that the problem is slightly more difficult than that. Image Understanding

13. Ridiculously brief history of computer vision 1966: Minsky assigns computer vision as an undergrad summer project 1960’s: interpretation of synthetic worlds 1970’s: some progress on interpreting selected images 1980’s: ANNs come and go; shift toward geometry and increased mathematical rigor 1990’s: face recognition; statistical analysis in vogue 2000’s: broader recognition; large annotated datasets available; video processing starts; vision & graphis; vision for HCI; internet vision, etc. Guzman ‘68 Ohta Kanade ‘78 Turk and Pentland ‘91

14. How vision is used now Examples of state-of-the-art

15. Optical character recognition (OCR) Digit recognition, AT&T labs http://www.research.att.com/~yannhttp://www.research.att.com/~yann/ Technology to convert scanned docs to text If you have a scanner, it probably came with OCR software License plate readers http://en.wikipedia.org/wiki/Automatic_number_plate_recognition

16. Face detection Many new digital cameras now detect faces – Canon, Sony, Fuji, …

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

18. 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… “

19. Vision-based biometrics “How the Afghan Girl was Identified by Her Iris Patterns” Read the storystory wikipedia

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

21. Object recognition (in mobile phones) Point & FindPoint & Find, NokiaNokia Google Goggles

22. The Matrix movies, Special effects: shape capture

23. Pirates of the Carribean, Industrial Light and Magic Special effects: motion capture

24. Sports Sportvision first down line Nice explanation on www.howstuffworks.comexplanationwww.howstuffworks.com http://www.sportvision.com/video.html

25. Smart cars Mobileye [wiki article] Mobileyewiki article – Vision systems currently in high-end BMW, GM, Volvo models – By 2010: 70% of car manufacturers. Slide content courtesy of Amnon Shashua

26. Google cars http://www.nytimes.com/2010/10/10/science/10google.html?ref=artificialintelligence

27. Vision-based interaction (and games) 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!Lee’s work at CMU multi-touch display DigimaskDigimask: put your face on a 3D avatar. “Game turns moviegoers into Human Joysticks”, “Game turns moviegoers into Human Joysticks”, CNET Camera tracking a crowd, based on this work.this work

28. Interactive Games: Kinect Object Recognition: http://www.youtube.com/watch?feature=iv&v=fQ59dXOo63ohttp://www.youtube.com/watch?feature=iv&v=fQ59dXOo63o Mario: http://www.youtube.com/watch?v=8CTJL5lUjHghttp://www.youtube.com/watch?v=8CTJL5lUjHg 3D: http://www.youtube.com/watch?v=7QrnwoO1-8Ahttp://www.youtube.com/watch?v=7QrnwoO1-8A Robot: http://www.youtube.com/watch?v=w8BmgtMKFbYhttp://www.youtube.com/watch?v=w8BmgtMKFbY 3D tracking, reconstruction, and interaction: http://research.microsoft.com/en- us/projects/surfacerecon/default.aspxhttp://research.microsoft.com/en- us/projects/surfacerecon/default.aspx

29. Vision in space 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.Computer Vision on Mars NASA'S Mars Exploration Rover Spirit NASA'S Mars Exploration Rover Spirit captured this westward view from atop a low plateau where Spirit spent the closing months of 2007.

30. Industrial robots Vision-guided robots position nut runners on wheels

31. Mobile robots http://www.robocup.org/ NASA’s Mars Spirit Rover http://en.wikipedia.org/wiki/Spirit_rover Saxena et al. 2008 STAIRSTAIR at Stanford

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

33. Prerequisites A good working knowledge of C/C++, Java or Matlab A good understand of math (linear algebra, basic calculus, basic probability) Willing to learn new stuffs (optimization, statistical learning etc.)

34. 34 Course Objectives Develop an Understanding of Basic Computer Vision Techniques Through Lecture, Study, and Exercises Develop an Understanding of Basic Computer Vision Techniques Through Lecture, Study, and Exercises Develop a Fluency With a Computer Vision (CV) Software. Develop a Fluency With a Computer Vision (CV) Software. Implement an Independent Computer Vision Project Which Demonstrates Your Ability to Integrate the Mathematical Theory With the Practical Issues Implement an Independent Computer Vision Project Which Demonstrates Your Ability to Integrate the Mathematical Theory With the Practical Issues

35. 35 CPS-803 Computer Vision PreRequisites As already given 1 Introduction CV and IP; applications ; images and imaging devices ; perspective projection ; binary image processing 2 Pattern Recognition Concepts; filtering and edge detection; color and shading, including 3D effects 3 Texture, IBM Veggie Vision, image database, motion, motion vectors, optical flow 4 Segmentation, 2D matching 5 3D perception; stereo and structured light; shape from shading, 3D sensing; 3D Transformations, Camera calibration 6 3D reconstruction, 3D Object modelling and matching 7 Augmented reality, review entire course

36. 36 Books TextBook 1. Computer Vision by Linda Shapiro and George Stockman, Prentice- Hall 2001 Reference 1.Computer Vision: a modern approach, Forsyth and Ponce, Prentice- Hall 2002 2.Digital Image Processing by R. C. Gonzalez and R. E. Woods, Addison Wesley, Second Ed., 2002.

37. 37 CSE803 Computer Vision Credits: 3 Class: – On every Thursday Phone: 03215109216 Office: Computer Science Building Office hours: Any working day in the morning or by appointment. Emails and telephone calls are not good for asking questions.

38. 38 Homework: There will be 3 homework assignments. Projects: Four projects. You may use any language available to you, but C is suggested. Exams: Three exams are planned: 2 OHTs and final. Grading: Homework and projects: 30%, all equally weighted. OHTs 15%each; final: 40%. Work Deposit: Work will be due at the beginning of the class. No late work will be accepted. In case of official leave work will be deposited in the next class. Final Exam: will be cumulative (complete syllabus) exam of 2 hours. Changes: This syllabus is subject to change. The changes will be announced in the class.

39. 39 Table 2: Project and Homework Schedule DayDateSubject Thursday 2 nd WkProject 1 (Binary Image Analysis) Handouts Thursday 4 th WkProject 1 due, Homework 1 Handouts Thursday 5 th WkHomework 1 due, Project 2(Calibration) Handouts Thursday 7 th WkProject 2 due, Homework 2 Handout Thursday 8 th WkHomework 2 due, Project 3 (Shape detection) Handout Thursday 10 th WkProject 3 due Thursday 10 th WkProject 4 (Face recognition) Handout Thursday 12 th WkProject 4 due Thursday 13 th WkHomework 3 handed out Thursday 14 th WkHomework 3 due

40. Grading Schemes Assignments (10%) Quizes (10%) Projects(10%) OHTs(30%) Final Comprehansive Exam (40%)

41. Project Student can work in a group of two Submit your code and project report Final presentation & in class demos Late policy: 20% reduction per day if you do not have good reasons

42. Other Information My email: khattakn@mcs.edu.pk My office: Trg Office Shoa ul Qammer Block Office hours: any time in the Morning or by appointment

43. Email Me Today Your background Vision, Graphics, machine learning, image processing Math (linear algebra, statistics, calculus, optimization, etc.) Coding (C++, java, matlab, etc.) Your research Interest? Master/Ph.D. (year)? Why do you take this class?