1 / 41

Lecture 7

Lecture 7. The Eye and Neuromorphic Vision Juan A. Leñero Bardallo 2012. Outline. The eye and the retina Artificial Vs biological vision systems Fundamentals of photo receptors Read-out strategies Examples of neuromorphic vision systems Further processing.

Download Presentation

Lecture 7

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Lecture 7 The Eye and NeuromorphicVision Juan A. LeñeroBardallo 2012

  2. Outline • The eye and the retina • Artificial Vs biological vision systems • Fundamentals of photo receptors • Read-out strategies • Examples of neuromorphic vision systems • Further processing

  3. Eyeball Cross Section and Retina

  4. Retina Cells

  5. Schematized Retinal Cells

  6. Outline • The eye and the retina • Artificial Vs biological vision systems • Fundamentals of photo receptors • Read-out strategies • Examples of neuromorphic vision systems • Further processing

  7. Frame-based Sensors Vs Biological Vision

  8. Notion of Frame Tframe y x Time Tintegration Tintegration<Tframe

  9. Dynamic Range The human eye has a dynamic range higher than 10 decades Rüedi et al, JSSC 2003 ”A 128x128 Pixel 120dB Dynamic-Range Vision-Sensor Chip for Image Contrast and Orientation Extraction” Bad choice of the integration time

  10. Outline • The eye and the retina • Artificial Vs biological vision systems • Fundamentals of photo receptors • Read-out strategies • Examples of neuromorphic vision systems • Further processing

  11. Depletion Region and Phototransduction

  12. Electromagnetic Spectrum Silicon

  13. Some Possible Photodetectors in CMOS Technology A. Moini. ‘Vision Chips’. Kluwer Academic Publishers, 2000.

  14. Photogates

  15. Logarithmic Photoreceptors (b) (a)

  16. Photoreceptors with Negative Feedback (a) (b)

  17. Delbrück’s Adaptative Photo Cell (I)

  18. Delbrück’s Adaptative Photo Cell (II) Non-linear element

  19. Classic Active Pixel Sensor (APS)

  20. Outline • The eye and the retina • Artificial vision Vs biological systems • Fundamentals of photo receptors • Read-out strategies • Examples of neuromorphic vision systems • Further processing

  21. Read-outStrategies: • Addressing/Scanning • ChargeCoupledDevices (CCD) • AddressEventRepresentation (AER)

  22. Addressing/Scanning Token System

  23. Charge Coupled Devices (CCD)

  24. Address Event Representation (AER) (I)

  25. Address Event Representation (AER) (II)

  26. Outline • The eye and the retina • Artificial Vs vision biological systems • Fundamentals of photo receptors • Read-out strategies • Examples of neuromorphic vision systems • Further processing

  27. Mahowald’s Pixel

  28. Retina Pixel Boahen

  29. Dynamic Vision Sensor (DVS) P. Lichtsteiner, C. Posch, and T. Delbrück, “A 128x128 120dB 15µs latencyasynchronous temporal contrast vision sensor,”IEEE J. Solid-State Circuits, vol. 43, no. 2, pp. 566–576, Feb. 2008.

  30. Common Source Amplifier Can used as comparator

  31. Examples of Biomorphic Vision Sensors • Spatio-Temporal Contrast Detector: http://folk.uio.no/juanle/Projects/Spatial_contrast_retina.html • Dynamic Vision Sensor: http://folk.uio.no/juanle/Projects/DVS.html

  32. Outline • The eye and the retina • Artificial vision Vs biological systems • Fundamentals of photo receptors • Read-out strategies • Examples of neuromorphic vision systems • Further processing

  33. Further Image Processing • Motion Detection • Features extraction

  34. Token Based Motion Processing: Reichardt Detector

  35. Intensity Based

  36. Features Extraction

  37. ’Difference of Gaussians’ Kernel (I)

  38. ’Difference of Gaussians’ Kernel (II)

  39. Result of Edge Detection

  40. 45 Degrees Edge Kernel (I)

  41. 45 Degrees Edge Kernel (II)

More Related