1. Can computers match human perception?
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.

2. Successes Computer vision algorithms rival humans
(from class discussion)
aligning images
face morphing (Conan)
super-res, filling in hole, inpainting
face recognition with very busy images, volume
texture synthesis
shape reconstruction?
navigation?
perception biases, optical illusion
pattern matching
autostitch, 3D shape recon, image search, scissors

3. Challenges CV still far behind (from class discussion)
image understanding
segmentation
3D shape
computers can’t drive?
making good photographs (composition)
recognition, tracking, segmentation, scene understanding, motion

4. Directions: Sensors and Imaging
Columbia’s Omnicam
Stanford multicamera array
HDR, multispectral imaging, high frame-rate, res

5. Directions: Detection and Retrieval
Video Google
Real-time faces (Viola/Jones)

6. Directions: Vision and Learning
Fergus, Perona, and Zisserman, CVPR 2003

7. Directions: Capturing Humans
Allen et al., Space of Human Body Shapes
Zhang et al., Spacetime Faces

8. Directions: Scene Reconstruction
Debevec et al., Facade