1. twelve visual perception

2. Processing in the human visual system (maybe, sort of) eye early vision (striate cortex) contour detection salienceattention object recognition (IT cortex) spatial memory (PP cortex) “what” pathway “where” pathway grouping

3. The eye Lens Depth of field Chromatic aberration Photoreceptors Rods Cones Fovea, macula, and periphery Retinal processing Gain control Edge enhancement? Simple motion detection lens/iris rods cones retinal ganglion

4. Photoreceptors Rods Found mostly in the macula and periphery Very sensitive to light But don’t detect color Cones Found in the fovea Less sensitive Color sensitive  Colors seem to fade in low light

5. Trichromacy Having different cones for every possible wavelength would be bad We just have three kinds of cones “Blue” cones: short wavelengths “Green” cones: intermediate wavelengths “Red” cones: long wavelengths However, their responses overlap The eye reduces all the wavelengths at a given pixel to just the total “amount” of “red”, “green”, and “blue”

6. Components of a color image

7. Evolution of the color system S to cortex

8. Evolution of the color system S to cortex L + intensity − chroma (B-Y) cool/warm

9. Intensity and chromaticity Color image Intensity (R+G+B)Chromaticity (B-Y) white = cool black = warm grey = neurtral

10. Evolution of the color system S to cortex I + intensity − chroma (B-Y) L − chroma (R-G) cool/warm

11. Intensity and chromaticity Color image Intensity Chromaticity (B-Y) white = cool black = warn grey = neutral Chromaticity (R-G) white = red black = green grey = balanced

12. Code [define gray [i → [color i i i]]] [define signed-gray [i → [gray [+ 128 [∕ i 2]]]]] [define b-y [c → [− [blue c] [∕ [+ [red c] [green c]] 2]]]] [map-bitmap [c → [signed-gray [b-y c]]] cones]

13. Code [define r-g [c → [− [red c] [green c]]]] [map-bitmap [c → [signed-gray [r-g c]]] cones]

14. Image-level processing Pixel-level processing Massively parallel Preattentive Very fast

15. Contrast versus absolute intensity The visual system responds less to the amount of light or color Than to variations in the amount This is also known as contrast

16. Contrast detection Many of the neurons in the visual system are designed to respond to contrast Center/surround receptive fields Stimulated by the presence of light in a specific area Inhibited by its presence in the area immediately around it − + inhibitory region (light prevents firing) excitatory region (light stimulates firing)

17. Edge detection When applied to image intensity (R+G+B), Center/surround processing responds to lightness contrast Which often occurs at object edges

18. Scale space Contrast detection is performed at many different scales

19. Oriented edge detection Other neurons Are stimulated by light in an elongated region And inhibited by it in the region next to it These can detect the orientation of edges −+ −+ −+

20. Oriented edges

21. Constancy Illumination varies over a surface in intensity in chroma But you (usually don’t perceive the color of the surface as changing This is called perceptual constancy Color constancy Lightness constancy Georges de la tour, The Repentant Magdalene, C. 1640

22. Failures of lightness constancy

25. Failures of color constancy

29. Depth cues The world is 3D But the image is 2D Your vision system uses depth cues to recover lost information Stereopsis Motion Linear perspective Aerial perspective Relative size

30. Occlusion The interruption of edges of one object by the edges of another object Shows the interrupting object is in front C. Coles Phillips, Brittish Tank on Fifth Avenue, 1917

31. Stereo vision Your eyes are in slightly different positions So they receive slightly different images The disparity between images decreases with distance (simple case) So by matching features between the two eyes Your brain computes disparity And hence, depth

32. Motion parallax Objects farther away seem to move slower So the background seems to remain stable And the foreground moves Or, the opposite, if the camera tracks the foreground This is frequently used in cell animation

33. Linear perspective Parallel lines converge at a vanishing point One-point perspective One set of lines converging Gives a sense of immersion in the scene Background seems to pop out Two-point perspective Two sets of converging lines The object seems to pop out Three-point perspective Rarely used Very dramatic

34. Aerial perspective Surfaces get hazier as they recede into the distance

35. Relative size Apparent size decreases with distance So

36. Texture gradients Textures get finer as objects recede

37. Gestalt theory Theory of how we organize components of a percept into wholes Max Wertheimer, Kurt Koffka, Wolfgang Köhler Law of prägnanz (simplicity) We choose the simplest possible interpretation of a scene Formulated laws of grouping Disks or pacman?

38. Grouping by proximity

39. Grouping by similarity

40. Grouping by continuity

41. Grouping by closure

42. Grouping by common fate

44. Figure and ground We tend to separate the scene into One or more objects (“figure”), in front of or on top of A ground plane or background (“ground”) We use a number of tricks to do it Depth, grouping But it can be hard in degenerate cases

45. Attention Remember our fovea is only about 2° So we have to view scenes by scanning the fovea over the image Attention is driven both top-down by cognition Task Knowledge, expectations And bottom-up by Size Intensity Local contrast Motion Odd-man-out As well as recency (don’t look if we’ve just looked there)

46. Attention and scale space Attention starts at coarse (large) scales And narrows to finer scales

47. Attention and intensity All things being equal, our attention is driven to intense stimuli Bright lights Saturated colors Rapid motion

48. Attention and color Warm, bright colors draw attention Cooler, darker colors tend to recede into the background Claude Monet, Sunrise, 1872

49. Attention and contrast But one of the biggest drivers of attention is contrast We focus on unusual elements of the scene Elements that are different from their neighbors

50. Attention follows curves

51. Attention follows perspective and depth

52. Top-down attention Attention is also driven by expectations If I see something face-like I look for the facial features Or vice-versa And by task

53. Change blindness We only really see what the fovea is fixating If anything changes, we don’t notice unless we see motion Oh, and we don’t see motion while our eye is moving … Continuity in films Pretty woman Neisser et al’s demo Neisser et al’s demo Simons and Chabris’ demo SupplyPattern Work area Adapted from Ballard et al.

54. Shape recognition We don’t really know how this works It’s really hard … And there are a lot of high level semantic effects