2. Computer Vision No. 1 What is the Computer Vision?

3. Instructor u Katsushi Ikeuchi u Pointers: 03-5452-6242 cvl-sec@iis.u-tokyo.ac.jp 4-6-1 Komaba Meguro-ku http://www.cvl.iis.u-tokyo.ac.jp

4. Evaluation u attendance 50% u report 50%

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

6. Katsu Ikeuchi U. Tokyo Human visual system MIT AI Shape-from-shading 1978198019861996 ETL Object recognition CMU Assembly plan from observation Modeling from reality U. Tokyo Virtual heritage

7. Demonstration Videos

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

9. Bin Picking

10. Assembly Plan from Observation (1990)

11. Recent Result Assembly plan from observation

13. Learning Human Dance

14. Motion Capture Data

15. Robot Dancing

16. Modeling Cultural Heritage

17. Virtual City Probe Info

18. Virtual City Speed ： 10km/h Vehicle Pedestrian Near Yoyogi park

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

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

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

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

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

24. Pattern Recognition Decision making: mathematical theories

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

26. Image Understanding Scene description

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

28. Image Foggy golden triangle in Pittsburgh

29. But …

30. A lot of data u Landsat image –1scene: 3300 x 2300 x 4 = 30000000 bytes –200 scenes/ day u Color TV image –512 x 512 x 3 x 30 = 25000000 bytes/sec

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

32. Illusion due to the projection

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

34. Image u A image is a matrix of pixels u Each pixel –brightness –Color –Distance

35. Inside and Outside (Gestalt)

36. Common sense u To formulate the common sense → research topics

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

38. Application areas

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

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

42. 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#2 Dr. Vanno and Dr. Ogawara

43. 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