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Can computers match human perception?

This discussion explores the remarkable successes and ongoing challenges in computer vision, including algorithms that rival human perception. Key topics include face morphing, super-resolution techniques, and real-time face recognition in complex environments. We analyze perception biases, optical illusions, and the segmentation needed for effective image understanding. The advancements in sensors and imaging technologies, such as Columbia's Omnicam and Stanford's multicamera arrays, are highlighted, as are the prospects for navigation and scene reconstruction.

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Can computers match human perception?

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Presentation Transcript

  1. Can computers match human perception?

  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

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

  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 Zhang et al., Spacetime Faces Allen et al., Space of Human Body Shapes

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

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