1. Physiology of Vision: a swift overview 16-721: Learning-Based Methods in Vision A. Efros, CMU, Spring 2009 Some figures from Steve Palmer

2. Image Formation Digital Camera The Eye Film

3. Monocular Visual Field: 160 deg (w) X 135 deg (h) Binocular Visual Field: 200 deg (w) X 135 deg (h)

4. Figures © Stephen E. Palmer, 2002 What do we see? 3D world2D image

5. What do we see? 3D world2D image Painted backdrop

6. The Plenoptic Function Q: What is the set of all things that we can ever see? A: The Plenoptic Function (Adelson & Bergen) Let’s start with a stationary person and try to parameterize everything that he can see… Figure by Leonard McMillan

7. Grayscale snapshot is intensity of light Seen from a single view point At a single time Averaged over the wavelengths of the visible spectrum (can also do P(x,y), but spherical coordinate are nicer) P(  )

8. Color snapshot is intensity of light Seen from a single view point At a single time As a function of wavelength P(  )

9. Spherical Panorama All light rays through a point form a ponorama Totally captured in a 2D array -- P(  ) Where is the geometry??? See also: 2003 New Years Eve http://www.panoramas.dk/fullscreen3/f1.html

10. A movie is intensity of light Seen from a single view point Over time As a function of wavelength P( ,t)

11. Space-time images x y t

12. Holographic movie is intensity of light Seen from ANY viewpoint Over time As a function of wavelength P( ,t,V X,V Y,V Z )

13. The Plenoptic Function Can reconstruct every possible view, at every moment, from every position, at every wavelength Contains every photograph, every movie, everything that anyone has ever seen! it completely captures our visual reality! Not bad for a function… P( ,t,V X,V Y,V Z )

14. The Eye is a camera The human eye is a camera! Iris - colored annulus with radial muscles Pupil - the hole (aperture) whose size is controlled by the iris What’s the “film”? –photoreceptor cells (rods and cones) in the retina

15. The Retina

16. Retina up-close Light

17. © Stephen E. Palmer, 2002 Cones cone-shaped less sensitive operate in high light color vision Two types of light-sensitive receptors Rods rod-shaped highly sensitive operate at night gray-scale vision

18. Rod / Cone sensitivity The famous sock-matching problem…

19. © Stephen E. Palmer, 2002 Distribution of Rods and Cones Night Sky: why are there more stars off-center?

20. Electromagnetic Spectrum http://www.yorku.ca/eye/photopik.htm Human Luminance Sensitivity Function

21. Why do we see light of these wavelengths? © Stephen E. Palmer, 2002 …because that’s where the Sun radiates EM energy Visible Light

22. The Physics of Light Any patch of light can be completely described physically by its spectrum: the number of photons (per time unit) at each wavelength 400 - 700 nm. © Stephen E. Palmer, 2002

23. The Physics of Light Some examples of the spectra of light sources © Stephen E. Palmer, 2002

24. The Physics of Light Some examples of the reflectance spectra of surfaces Wavelength (nm) % Photons Reflected Red 400 700 Yellow 400 700 Blue 400 700 Purple 400 700 © Stephen E. Palmer, 2002

25. The Psychophysical Correspondence There is no simple functional description for the perceived color of all lights under all viewing conditions, but …... A helpful constraint: Consider only physical spectra with normal distributions area mean variance © Stephen E. Palmer, 2002

26. The Psychophysical Correspondence MeanHue # Photons Wavelength © Stephen E. Palmer, 2002

27. The Psychophysical Correspondence VarianceSaturation Wavelength # Photons © Stephen E. Palmer, 2002

28. The Psychophysical Correspondence AreaBrightness # Photons Wavelength © Stephen E. Palmer, 2002

29. Three kinds of cones: Physiology of Color Vision Why are M and L cones so close?

30. Retinal Processing © Stephen E. Palmer, 2002

31. Single Cell Recording Microelectrode Amplifier Electrical response (action potentials) mV © Stephen E. Palmer, 2002

32. Single Cell Recording © Stephen E. Palmer, 2002

33. Retinal Receptive Fields Receptive field structure in ganglion cells: On-center Off-surround Stimulus condition Electrical response © Stephen E. Palmer, 2002

34. Receptive field structure in ganglion cells: On-center Off-surround Stimulus condition Electrical response Retinal Receptive Fields © Stephen E. Palmer, 2002

35. Receptive field structure in ganglion cells: On-center Off-surround Stimulus condition Electrical response Retinal Receptive Fields © Stephen E. Palmer, 2002

36. Receptive field structure in ganglion cells: On-center Off-surround Stimulus condition Electrical response Retinal Receptive Fields © Stephen E. Palmer, 2002

37. Receptive field structure in ganglion cells: On-center Off-surround Stimulus condition Electrical response Retinal Receptive Fields © Stephen E. Palmer, 2002

38. Receptive field structure in ganglion cells: On-center Off-surround Stimulus condition Electrical response Retinal Receptive Fields © Stephen E. Palmer, 2002

39. RF of On-center Off-surround cells Retinal Receptive Fields © Stephen E. Palmer, 2002

40. RF of Off-center On-surround cells Retinal Receptive Fields © Stephen E. Palmer, 2002 Surround Center

41. Retinal Receptive Fields

42. Receptive field structure in bipolar cells Light Retinal Receptive Fields © Stephen E. Palmer, 2002

43. Receptive field structure in bipolar cells Retinal Receptive Fields © Stephen E. Palmer, 2002

44. Visual Cortex aka: Primary visual cortex Striate cortex Brodman’s area 17 Cortical Area V1

45. Cortical Receptive Fields Single-cell recording from visual cortex David Hubel & Thorston Wiesel © Stephen E. Palmer, 2002

46. Cortical Receptive Fields Single-cell recording from visual cortex © Stephen E. Palmer, 2002

47. Cortical Receptive Fields Three classes of cells in V1 Simple cells Complex cells Hypercomplex cells © Stephen E. Palmer, 2002

48. Cortical Receptive Fields Simple Cells: “Line Detectors” © Stephen E. Palmer, 2002

49. Cortical Receptive Fields Simple Cells: “Edge Detectors” © Stephen E. Palmer, 2002

50. Cortical Receptive Fields Constructing a line detector © Stephen E. Palmer, 2002

51. Cortical Receptive Fields Complex Cells 0o0o © Stephen E. Palmer, 2002

52. Cortical Receptive Fields Complex Cells 60 o © Stephen E. Palmer, 2002

53. Cortical Receptive Fields Complex Cells 90 o © Stephen E. Palmer, 2002

54. Cortical Receptive Fields Complex Cells 120 o © Stephen E. Palmer, 2002

55. Cortical Receptive Fields Constructing a Complex Cell © Stephen E. Palmer, 2002

56. Cortical Receptive Fields Hypercomplex Cells © Stephen E. Palmer, 2002

57. Cortical Receptive Fields Hypercomplex Cells © Stephen E. Palmer, 2002

58. Cortical Receptive Fields Hypercomplex Cells © Stephen E. Palmer, 2002

59. Cortical Receptive Fields Hypercomplex Cells “End-stopped” Cells © Stephen E. Palmer, 2002

60. Cortical Receptive Fields “End-stopped” Simple Cells © Stephen E. Palmer, 2002

61. Cortical Receptive Fields Constructing a Hypercomplex Cell © Stephen E. Palmer, 2002

62. Mapping from Retina to V1

63. Why edges? So, why “edge-like” structures in the Plenoptic Function?