1. No. 1 What is the Computer Vision?

2. Instructor Katsushi Ikeuchi Pointers: 03-5452-6242
4-6-1 Komaba Meguro-ku

3. Evaluation
attendance 50%
report 50%

4. Schedule Shape-from-X Interpretation Special topics
Analysis of line-drawing
Shape-from-shading
Binocular stereo
Interpretation
Interpolation
Representation
Special topics
Modeling from reality

5. Katsu Ikeuchi U. Tokyo ETL U. Tokyo MIT AI CMU Human visual system
Object recognition
U. Tokyo
Virtual heritage
MIT AI
Shape-from-shading
CMU
Assembly plan from observation
Modeling from reality
1978
1980
1986
1996

6. Demonstration Videos

7. Photometric Stereo (1980) Brightness difference -> 3D shape
3D shape -> 3D Pose determination
3DPose -> Grasping

8. Bin Picking

9. Assembly Plan from Observation (1990)

10. Recent Result Assembly plan from observation

12. Learning Human Dance

13. Motion Capture Data

14. Robot Dancing

15. Modeling Cultural Heritage

16. Virtual City
Probe Info　

17. Virtual City Speed：10km/h Vehicle Pedestrian Vehicle Near Yoyogi park
ampm-1.png
Vehicle
Pedestrian
Speed：10km/h
Near Yoyogi park
Vehicle

18. Computer Vision (CV)
To make a computer to recognize the 3D world as we do
To generate 3D representations from 2D images

19. CV and related areas Image Understanding (AI) Pattern Recognition
(Mathematical theories)
Image Processing
(Signal processing)

20. CV and related areas Image Understanding (AI) Pattern Recognition
(Mathematical theories)
Image Processing
(Signal processing)

21. To get better images: 2D-to-2D
Image Processing
To get better images: 2D-to-2D

22. CV and related areas Image Understanding (AI) Pattern Recognition
(Mathematical theories)
Image Processing
(Signal processing)

23. Decision making: mathematical theories
Pattern Recognition
Decision making: mathematical theories

24. CV and related areas Image Understanding (AI) Pattern Recognition
(Mathematical theories)
Image Processing
(Signal processing)

25. Image Understanding
Scene description

26. Why difficult ? A lot of data Ambiguity
Projection of a 3D world to a 2D image
Many factors to influence the image
Illumination condition
Object shape
Camera characteristics

27. Image
Foggy golden triangle in Pittsburgh

28. But …

29. A lot of data Landsat image Color TV image
1scene: 3300 x 2300 x 4 = bytes
200 scenes/ day
Color TV image
512 x 512 x 3 x 30 = bytes/sec

30. Why difficult ? A lot of data Ambiguity
Projection of a 3D world to a 2D image
Many factors to influence the image
Illumination condition
Object shape
Camera characteristics

31. Illusion due to the projection

32. Why difficult ? A lot of data Ambiguity
Projection of a 3D world to a 2D image
Many factors to influence to the image
Illumination condition
Object shape
Camera characteristics

33. Image A image is a matrix of pixels Each pixel brightness Color
Distance

34. Inside and Outside (Gestalt)

35. Common sense
To formulate the common sense
→ research topics

36. Current issues A lot of data Ambiguity Many factors
Computational sensor
Vision board
Ambiguity
Projective geometry
constraints
Many factors
Physics-based vision

37. Application areas

38. Application areas

39. What is Computer Vision?
Vision is … an information processing task that constructs efficient symbolic descriptions of the world from images. (Marr)
Vision is … inverse graphics.
Vision is … looks easy, but is difficult.
Vision is … difficult, but is fun. (Kanade)
Vision is an engineering science to create
an alternative of human visual systems on computers （Ikeuchi）

40. References Journals Inter. J. Computer Vision
IEEE Trans. Pattern Analysis and Machine Intelligence
IEICE D-2
IPSJ Trans CVIM
International conferences
Inter. Conf. Computer Vision (ICCV)
Computer Vision and Pattern Recognition (CVPR)
Asian Conf. Computer Vision (ACCV)
Special interest groups
IPSJ CVIM
IEICE PRMU

41. Schedule (April-May) 4/12 Introduction 4/19 Line drawing
4/26 Perspective projection
5/3 Holiday
5/10 Shape from Shading
5/17 Color Dr. Miyazaki
5/24 Stereo#1
5/31 Stereo# Dr. Vanno and Dr. Ogawara

42. Schedule (June-July) 6/7 Motion analysis 6/14 No class
6/21 EPI, IBR & MBR Dr. Ono
6/28 Interpolation
7/5 Object representation#1 Dr.Takamatsu
7/12 Object representation#2