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Computational Architectures in Biological Vision, USC, Spring 2001

Explore sampling grids, visual pathways, and retinal processing in computational vision architectures. Discuss challenges and approaches in biological and engineering designs, including retinal implants and silicon retina technologies.

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Computational Architectures in Biological Vision, USC, Spring 2001

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  1. Computational Architectures in Biological Vision, USC, Spring 2001 • Lecture 4: Introduction to Vision. • Reading Assignments: • Chapters 2 and 3 of textbook.

  2. Today’s lecture • The challenges: • Optics and image formation • Sampling and image representation • Theoretical limits • The biological approach: • Organization of the primate retina • Trading accuracy for coverage: moving eyes • The engineering approach: • Arrays of photosensitive sensors • On-board processing and VLSI sensors • Trading accuracy for coverage: multiple moving cameras

  3. Projection

  4. Projection

  5. Convention: Visual Angle • Rather than reporting two numbers (size of object and distance to observer), we will combine both into a single number: • visual angle • e.g., the moon: about 0.5deg visual angle • your thumb nail at arm’s length: about 1.5deg visual angle • 1deg visual angle: 0.3mm on retina

  6. Charge-Coupled Devices • Uniform array of sensors • Very little on-board processing • Very inexpensive

  7. Optics limitations: acuity

  8. Sampling

  9. Sampling

  10. Sampling

  11. The Big Question

  12. Convolution & Fourier Transforms

  13. Sampling in the frequency domain

  14. Reconstruction

  15. Aliasing

  16. Sampling Theorem

  17. Aliasing

  18. Eye Anatomy

  19. Visual Pathways

  20. Image Formation Accomodation: ciliary muscles can adjust shape of lens, yielding an effect equivalent to an autofocus.

  21. Phototransduction Cascade • Net effect: light (photons) is transformed into electrical (ionic) current.

  22. Rods and Cones • Roughly speaking: 3 types of cones, sensitive to red, green and blue.

  23. Processing layers in retina

  24. Retinal Processing

  25. Center-Surround • Center-surround organization: neurons with receptive field at given location receive inhibition from neurons with receptive fields at neighboring locations (via inhibitory interneurons).

  26. Early Processing in Retina

  27. Color Processing

  28. Over-representation of the Fovea • Fovea: central region of the retina (1-2deg diameter); has much higher density of receptors, and benefits from detailed cortical representation.

  29. Fovea and Optic Nerve

  30. Blind Spot

  31. Retinal Sampling

  32. Retinal Sampling

  33. Seeing the world through a retina

  34. Sampling & Optics Because of blurring by the optics, we cannot see infinitely small objects…

  35. Sampling & optics The sampling grid optimally corresponds to the amount of blurring due to the optics!

  36. Retinal Implants Problems: - long-term toxicity (neurons close to electrodes die) - image quality so low that users hate it!

  37. Silicon Retina • Smoothing network: allows system to adapt to various light levels.

  38. VLSI sensor vith retinal organization

  39. Combining cameras • From fixed cameras with overlapping field of view, we can reconstruct a virtual camera with any point of view: virtual PTZ (pan-tilt-zoom).

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