1. Colour an algorithmic approach Thomas Bangert thomas.bangert@qmul.ac.uk http://www.eecs.qmul.ac.uk/~tb300/pub/PhD/ColourVision2.pptx PhD Research Topic

2. understanding how natural visual systems process information Visual system: about 30% of cortex most studied part of brain best understood part of brain

3. Image sensors  Binary sensor array monochromatic ‘external retina’  Luminance sensor array dichromatic colour  Multi-Spectral sensor array tetrachromatic colour What do these direct links to the brain do?

4. Lets hypothesise … When an astronomer looks at a star, how does he code the information his sensors produce? It was noticed that parts of spectrum were missing.

5. Looking our own star – the sun x

6. Each atomic element absorbs at specific frequencies …

7. We can Code for these elements … We can imagine how coding spectral element lines could be used for visual perception … by a creature very different to us … a creature which hunts by ‘tasting’ the light we reflect … seeing the stuff we are made of Colour in this case means atomic structure and chemistry…

8. Where do we start with humans? Any visual system starts with the sensor. What kind of information do these sensors produce? How do we use that information to code what is relevant to us? Let’s first look at sensors we ourselves have designed!

9. Sensors we build X Y

10. The Pixel Sensors element may be:  Binary  Luminance  RGB The fundamental unit of information!

11. The Bitmap 2-d space represented by integer array 0 12 0 1

12. What information is produced? 2-d array of pixels:  Black & White Pixel: –single luminance value, usually 8 bit  Colour Pixel –3 colour values, usually 8-bit RGB

13. What does RGB mean? It is an instruction for producing light stimuli Light stimuli for a human standard observer Light stimuli produce perception RGB codes the re-production of measured perceptual stimuli It is assumed that humans are trichromatic It tells us nothing about what colour means!

14. The Standard Observer CIE1931 xy chromaticity diagram primaries at: 435.8nm, 546.1nm, 700nm The XYZ sensor response now we extract the colour information from the sensor readings The Math: … 2-d as z is redundant

15. Understanding CIE chromaticity White in center Saturated / monochromatic colours on the periphery Best understood as a failed colour circle Everything in between is a mix of white and the colour

16. Does it match? The problem of ‘negative primaries’ But does it blend? Monochromatic Colours

17. What the Human Visual System (HVS) does is very different! ?

18. Human Visual System (HVS) Part 1 Coding Colour

19. The Sensor 2 systems: day-sensor & night-sensor To simplify: we ignore night sensor system Cone Sensors very similar to RGB sensors we design for cameras

20. BUT: sensor array is not ordered arrangement is random note: very few blue sensors, none in the centre

21. sensor pre-processing circuitry

22. First Question: What information is sent from sensor array to visual system? Very clear division between sensor & pre-processing (Front of Brain) and visual system (Back of Brain) connected with very limited communication link

23. Receptive Fields All sensors in the retina are organized into receptive fields Two types of receptive field. Why?

24. What does a receptive field look like? In the central fovea it is simply a pair of sensors. Always 2 types: plus-centre minus-centre

25. What do retinal receptive fields do? Produce an opponent value: simply the difference between 2 sensors This means: it is a relative measure, not an absolute measure and no difference = no information to brain

26. Sensor Input Luminance Levels it is usual to code 256 levels of luminance Linear: Y Logarithmic: Y’

27. Receptive Field Function - - - - - - - - - + + + + + + + + + - - - - - - - - - + + + + + + + + + - - - - - - - - - + + + + + + + + + Min Zone Max-Min Function Output is difference between average of center and max/min of surround Max Zone Tip of Triangle

28. Dual Response to gradients Why? Often described as second derivative/zero crossing

29. Abstracted Neurons only produce positive values. Dual +/- produces positive & negative values. Together: called a channel means signed values. Produces directional information Location, angle luminance, equiluminance and colour Information sent to higher visual processing areas This is a sparse representation From this the percept is created This is a type of data compression. Only essential information is sent! Conversion from this format to bitmap?

30. starting with the sensor: Human Sensor Response to non-chromatic light stimuli

31. HVS Luminance Sensor Idealized A linear response in relation to wavelength. Under ideal conditions can be used to measure wavelength.

32. Spatially Opponent HVS: Luminance is always measured by taking the difference between two sensor values. Produces: contrast value Which is done twice, to get a signed contrast value

33. Moving from Luminance to Colour Primitive visual systems were in b&w Night-vision remains b&w Evolutionary Path –Monochromacy –Dichromacy(most mammals – eg. the dog) –Trichromacy (birds, apes, some monkeys) Vital for evolution: backwards compatibility

34. Electro-Magnetic Spectrum Visible Spectrum Visual system must represent light stimuli within this zone.

35. Colour Vision Young-Helmholtz Theory Argument: Sensors are RGB therefore Brain is RGB  3 colour model

36. Hering colour opponency model Fact: we never see reddish green or yellowish blue. Therefore: colours must be arranged in opponent pairs: Red  Green Blue  Yellow  4 colour model

37. Colour Sensor response to monochromatic light Human Bird 4 sensors Equidistant on spectrum

38. How to calculate spectral frequency with 2 poor quality luminance sensors. Roughly speaking: Sensor Value Wavelength 0.8 0.6 0.2 0.0 1.0 0.4 λ-Δλ-Δλ λ+Δλ+Δ R G a shift of Δ from a known reference point

39. the ideal light stimulus Monochromatic Light Allows frequency to be measured in relation to reference.

40. Problem: natural light is not ideal Light stimulus might not activate reference sensor fully. Light stimulus might not be fully monochromatic. ie. there might be white mixed in

41. Solution: A 3 rd sensor is used to measure equiluminance. Which is subtracted. Then reference sensor can be normalized

42. Equiluminance & Normalization Also called Saturation and Lightness. Must be removed first – before opponent values calculated. Then opponent value = spectral frequency Values must be preserved – otherwise information is lost.

43. a 4 sensor design 2 opponent pairs only 1 of each pair can be active min sensor is equiluminance

44. What is Colour? Colour is calculated exactly the same as luminance contrast. The only difference is spectral range of sensors is modified. Colour channels are: RGRG ByBy Uncorrected colour values are contrast values. But with white subtracted and normalized: Colour is Wavelength!

45. How many sensors? 4 primary colours require 4 sensors!

46. Human Retina only has 3 sensors! What to do? We add an emulation layer. Hardware has 3 physical sensors but emulates 4 sensors No maths … just a diagram!

47. Testing Colour Opponent model What we should see What we do see Unfortunately it does not match There is Red in our Blue

48. Pigment Absorption Data of human cone sensors Red > Green

49. Solution: HVS colour representation must be circular! Which is not a new idea, but not currently in fashion. 540nm 620nm 480nm

50. Dual Opponency with Circularity an ideal model using 2 sensor pairs

51. … requires 2 independent channels  which give 4 primary colours Yellow added as a primary! Which allows a simple transform to circular representation

52. Opponent Values  Hue A simple transform from 2 opponent values to a single hue value How might HVS do this? we keep 2 colour channels but link them

53. Travelling the Colour Wheel (Hue) One Chroma channel is always at max or min The other Chroma channel is incremented or decremented Rules: if (C B ==Max)C R -- if (C R ==Max)C B ++ if (C R ==Min)C B -- if (C B ==Min)C R ++ +-

54. Colour Wheel Simple rule based system that cycles through the colour wheel Allows arithmetic operations on colour

55. Part 2 Accurate Colour reproduction First problem: Real world is not monochromatic Spectrum of a common yellow flower

56. Accurate Colour reproduction second problem: human colour vision is inaccurate prone to ‘making stuff up’ varies from person to person The closer the sensors the less accurate the color information. All humans are to an extent color blind … compared to animals like birds.

57. Examples of real world colour? Colours are often computed, not measured!

58. … an extreme example What is the colour?

59. Accurate colour reproduction … for dual channel opponency Problem # 1 very easy to solve we simply assume monochromacy when stimuli are not monochromatic opponent channels simply subtract to 0 green, yellow and red are active r-g = 0 b = 0 leaving only yellow stimuli equivalent to monochromatic

60. Accurate colour reproduction … with primaries Only primaries are true colours all other colours are intermediary … and can be generated by proportions of primaries!

61. Accurate colour reproduction … for humans Any colour may be displayed by a combination of 2 primaries but the location of primaries can vary between individuals and intermediary locations can be distorted Problem # 2

62. Solution to Accurate colour reproduction … for the individual human 1.primaries must be mapped for the individual 2.mid-points must be mapped 467 517 573 644 503 603 545 Provides an individual colour profile … a map of the primaries and intermediary points. Can be repeated recursively for greater precision.

63. Does it work? Colour opponency requires primaries to be precisely located. For humans this would be virtual primaries Is there evidence that this is so? Do the colours match? This will be tested empirically …

64. monochromator Xenon light source equal light across visible spectrum Apparatus

65. Procedure generate subject selectable monochromatic stimuli subject selects colour virtual primaries are calculated

66. Preliminary results BlueGreenYellowRed 455520580650 465520580640 465520575640 470525585655 460520575640 465520575650 465525575635 475515570640 470522.5570635 482.5525572.5635 462.5532.5580640 471.25526578.3647 467.2522.6576.3642.3 7.194.5 6.7 Average Std Dev

67. Discussion small sample with inaccurate tools but primaries appear to be are very closely grouped and spaced equidistantly – exactly as predicted

68. In Future visits to optometrist will include a colour test Colour displays may be set by colour ‘prescription’

69. http://www.eecs.qmul.ac.uk/~tb300/pub/PhD/ColourVision2.pptx http://www.eecs.qmul.ac.uk/~tb300/pub/PhD/ColourVision2.pptx References Poynton, C. A. (1995). “Poynton’s Color FAQ”, electronic preprint. http://www.poynton.com/notes/colour_and_gamma/ColorFAQ.html http://www.poynton.com/notes/colour_and_gamma/ColorFAQ.html Bangert, Thomas (2008). “TriangleVision: A Toy Visual System”, ICANN 2008. Goldsmith, Timothy H. (July 2006). “What birds see”. Scientific American: 69–75. Neitz, Jay; Neitz, Maureen. (August 2008). “Colour Vision: The Wonder of Hue”. Current Biology 18(16): R700-r702. Questions?