1. Carlo Tomasi, Computer Science

2. Human Vision

3. Computer Vision?

4. Human Vision ?

5. Seeing is Interpretation

8. Seeing is Recognition Giuseppe Arcimboldo, 1526-1593

9. Computer Vision frog, mushroom reconstruction recognition

10. Reconstruction: Stereo

11. Triangulation is Easy

12. Correspondence is Hard ?

13. It Can be Done (More or Less)

14. Face Recognition

15. Recognition: Face Space

17. Hardware  HDTV video camera:  10M pixels, uniform  30-60 frames per second  20 Mbits per second (compressed)  Variable field of view up to 1/3 of a sphere  Human eye:  7M cones in the fovea, 120M rods  1.2M axons in the optic nerve  0.6Mbits per second (compressed)  1/3 of a sphere field of view  28 arcsec resolution (finger at 30 m)

18. Hardware  $1M buys you this from Dell:  10 12 operations per second  10 12 bytes of memory  10 15 bytes of disk space  10 12 bytes per second of communication [speed of light 3x10 8 m/s]  One human brain gets you this:  10 12 neurons  10 15 synapses (connections) [speed of action potential 10 0 -10 2 m/s]

19. Comparing Apples and Oranges  David Marr, 1945-1980, Cambridge (UK), MIT  The levels:  Computational: What  Algorithmic: How  Mechanical: Wherewith  Computational goals are the same  Algorithms can be similar  Brains are essentially parallel  Computers are essentially sequential  Mechanical substrate is different

20. Paying the Bills