Can computers match human perception?

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Presentation transcript:

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.

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

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

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

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

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

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

Directions: Scene Reconstruction Debevec et al., Facade

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