Upgrade to Pro — share decks privately, control downloads, hide ads and more …

Human Perception and Colour Theory - Lecture 2 - Information Visualisation (4019538FNR)

Human Perception and Colour Theory - Lecture 2 - Information Visualisation (4019538FNR)

This lecture forms part of the course Information Visualisation given at the Vrije Universiteit Brussel.

1135dc242dcff3b90ae46fc586ff4da8?s=128

Beat Signer
PRO

February 18, 2021
Tweet

Transcript

  1. 2 December 2005 Information Visualisation Human Perception and Colour Theory

    Prof. Beat Signer Department of Computer Science Vrije Universiteit Brussel beatsigner.com
  2. Beat Signer - Department of Computer Science - bsigner@vub.be 2

    February 18, 2021 Information Visualisation Process Data Representation Data Data Presentation Interaction mapping perception and visual thinking
  3. Beat Signer - Department of Computer Science - bsigner@vub.be 3

    February 18, 2021 General Guidelines Design graphic representations of data by taking into account human sensory capabilities in such a way that important data elements and data patterns can be quickly perceived. Important data should be represented by graphical elements that are more visually distinct than those representing less important information. Greater numerical quantities should be represented by more distinct graphical elements.
  4. Beat Signer - Department of Computer Science - bsigner@vub.be 4

    February 18, 2021 Model of Perceptual Processing "What" system
  5. Beat Signer - Department of Computer Science - bsigner@vub.be 5

    February 18, 2021 Model of Perceptual Processing ... ▪ Stage 1: Parallel processing to extract low-level features ▪ billions of neurons working in parallel (in eye and visual cortex) ▪ orientation, colour, texture and motion ▪ results in a set of feature maps ▪ Stage 2: Pattern perception ▪ slower serial processing ▪ continuous contours, regions of same colour or texture ▪ two-visual-system theory: "Action" system and "What" system ▪ Stage 3: Visual working memory ▪ only a few objects in visual working memory ▪ may provide answers to visual query ▪ Attention affects all three stages!
  6. Beat Signer - Department of Computer Science - bsigner@vub.be 6

    February 18, 2021 Costs and Benefits of Visualisation Where two or more tools can perform the same task, choose the one that allows for the most valuable work to be done per unit time. Consider adopting novel design solutions only when the estimated payoff is substantially greater than the cost of learning to use them. Unless the benefit of novelty outweighs the cost of inconsistency, adopt tools that are consistent with other commonly used tools.
  7. Beat Signer - Department of Computer Science - bsigner@vub.be 7

    February 18, 2021 Visible Light Observing is interpreting light patterns https://commons.wikimedia.org/wiki/File:EM_spectrum.svg
  8. Beat Signer - Department of Computer Science - bsigner@vub.be 8

    February 18, 2021 Gibson's Ecological Optics (1979) ▪ Extract properties of surfaces ▪ colour, texture, ... ▪ Ambient optical array ▪ spherical array of light arriving at a given point ▪ Optical flow ▪ dynamic ambient optical array - moving objects, moving viewpoint ▪ perception of motion patterns Ambient optical array [Information Visualization, Colin Ware, 2013] Consider texturing surfaces in 3D visualisations.
  9. Beat Signer - Department of Computer Science - bsigner@vub.be 9

    February 18, 2021 Anatomy of the Human Eye https://commons.wikimedia.org/wiki/File:Blausen_0388_EyeAnatomy_01.png Fovea
  10. Beat Signer - Department of Computer Science - bsigner@vub.be 10

    February 18, 2021 Anatomy of the Human Eye ... ▪ Variable focus lens ▪ Pupil (aperture) ▪ Retina (sensor array) ▪ what we see ≠ image on the retina ▪ two types of cells - rods (~100 million), highly sensitive at low light levels - cones (~6 million), three distinct colour receptors (S-cones, M-cones and L-cones) (trichromacy) ▪ Brain forms our sight ▪ focal length ▪ maximum sharpness ▪ …
  11. Beat Signer - Department of Computer Science - bsigner@vub.be 11

    February 18, 2021 Focus ▪ Focus = focus distance ▪ Depth of focus ▪ distance eye-object = 50cm - focus lies between 43cm and 60cm ▪ distance eye-object = 3m - focus lies between 1.5m and infinite
  12. Beat Signer - Department of Computer Science - bsigner@vub.be 12

    February 18, 2021 Visual Field Visual field [Information Visualization, Colin Ware, 2013]
  13. Beat Signer - Department of Computer Science - bsigner@vub.be 13

    February 18, 2021 Optimal Screen
  14. Beat Signer - Department of Computer Science - bsigner@vub.be 14

    February 18, 2021 Optimal Screen … Use a high-resolution display with a moderate viewing angle (e.g. 40 degrees) for data analysis. This applies both to individual data analysis when the screen can be on a desktop and close to the user and to collaborative data analysis when the screen must be larger and farther away.
  15. Beat Signer - Department of Computer Science - bsigner@vub.be 15

    February 18, 2021 Visual Acuity ▪ Eyesight = retinal focus + brain interpretation ▪ Extent to which we can perceive details ▪ ability to identify black symbols on a white back- ground at a standardised distance when the size of the symbols is varied ▪ Important for the maxi- mum density of data on a screen
  16. Beat Signer - Department of Computer Science - bsigner@vub.be 16

    February 18, 2021 Brightness ▪ Colour space can be divided into luminance (grey scale) and two chromatic (hue and saturation) dimensions ▪ Light receptors in the eye ▪ do not measure the amount of light on the retina ▪ measure relative light changes over time and over adjacent spots on the retina ▪ Eye is a change meter rather than a light meter ▪ Luminance refers to the measured amount of light coming from some region of space
  17. Beat Signer - Department of Computer Science - bsigner@vub.be 17

    February 18, 2021 Brightness Illusions ▪ Retinal ganglion cell receives input from multiple receptors (receptive field) ▪ Processing tries to enhance edges ▪ lateral inhibition from neighbouring neurons Herman grid illusion [Information Visualization, Colin Ware, 2013]
  18. Beat Signer - Department of Computer Science - bsigner@vub.be 18

    February 18, 2021 Simultaneous Brightness Contrast Simultaneous brightness contrast [Information Visualization, Colin Ware, 2013]
  19. Beat Signer - Department of Computer Science - bsigner@vub.be 19

    February 18, 2021 Chevreul Illusion Chevreul illusion [Information Visualization, Colin Ware, 2013]
  20. Beat Signer - Department of Computer Science - bsigner@vub.be 20

    February 18, 2021 Simultaneous Contrast and Errors ▪ Simultaneous contrast effects result in large errors for quantitative information in grey scale ▪ up to 20% error for map on the right Contrast effects [Information Visualization, Colin Ware, 2013] Avoid using grey scale as a method for representing more than a few (two to four) numerical values.
  21. Beat Signer - Department of Computer Science - bsigner@vub.be 21

    February 18, 2021 Contrast Effects and Shading ▪ Computer graphics shading algorithms ▪ uniform (flat) shading - Chevreul illusion ▪ Gouraud shading - Mach banding ▪ Phong shading - smooth changes with no appreciable Mach banding Shading [Information Visualization, Colin Ware, 2013] uniform (flat) shading Gouraud shading Phong shading
  22. Beat Signer - Department of Computer Science - bsigner@vub.be 22

    February 18, 2021 Edge Enhancement Consider using Cornsweet contours instead of simple lines to define convoluted bounded regions.
  23. Beat Signer - Department of Computer Science - bsigner@vub.be 23

    February 18, 2021 Highlighting via Contrast Consider using adjustments in luminance contrast as a highlighting method. It can be applied by reducing the contrast of unimportant items or by locally adjusting the background to increase the luminance contrast of critical areas. Highlighting [Information Visualization, Colin Ware, 2013]
  24. Beat Signer - Department of Computer Science - bsigner@vub.be 24

    February 18, 2021 Contrast Crispening ▪ More subtle grey values can be distinguished at the point of crossover If subtle grey-level gradations within the bounds of a small object are important, create low-luminance contrast between the object and its background. Crispening [Information Visualization, Colin Ware, 2013]
  25. Beat Signer - Department of Computer Science - bsigner@vub.be 25

    February 18, 2021 Monitor/Projector Setup Ideally, when setting up a monitor for viewing data, a light neutral- coloured wall behind the screen should reflect an amount of light comparable to the level of light coming from the monitor. The wall facing the screen should be of low reflectance (mid- to dark grey) to reduce reflections from the monitor screen. Lights should be placed so that they do not reflect from the monitor screen. When setting up a room for a projection system, ensure that minimal room light falls on the projector screen. This can be done by means of baffles to shield the screen from direct illumination. Low- reflectance (mid- to dark grey) walls are also desirable, as the walls will scatter light, some of which inevitably reaches the screen.
  26. Beat Signer - Department of Computer Science - bsigner@vub.be 26

    February 18, 2021 Colour Breaking Camouflage Finding the cherries [Information Visualization, Colin Ware, 2013]
  27. Beat Signer - Department of Computer Science - bsigner@vub.be 27

    February 18, 2021 Cone Cell Sensitivity
  28. Beat Signer - Department of Computer Science - bsigner@vub.be 28

    February 18, 2021 Colour Blindness ▪ About 10% of the male population and about 1% of the female population have some form of colour vision deficiency ▪ Most commonly a lack of either the L-cones (protanopia) or the M-cones (deuteranopia) ▪ both of these result in an inability to distinguish red and green
  29. Beat Signer - Department of Computer Science - bsigner@vub.be 29

    February 18, 2021 Colour Measurement ▪ C=rR+gG+bB
  30. Beat Signer - Department of Computer Science - bsigner@vub.be 30

    February 18, 2021 Colour Spaces ▪ Chromaticity coordinates ▪ hue (h) and saturation (s) ▪ luminance is treated separately ▪ Different colours spaces defined by different trian- gles (3 primary colours) ▪ sRGB ▪ RGB ▪ ...
  31. Beat Signer - Department of Computer Science - bsigner@vub.be 31

    February 18, 2021 Saturation of Small/Large Areas Use more saturated colours when colour coding small symbols, thin lines, or other small areas. Use less saturated colours for coding large areas.
  32. Beat Signer - Department of Computer Science - bsigner@vub.be 32

    February 18, 2021 Saturation If using colour saturation to encode numerical quantity, use greater saturation to represent greater numerical quantities. Avoid using a saturation sequence to encode more than three values.
  33. Beat Signer - Department of Computer Science - bsigner@vub.be 33

    February 18, 2021 Luminance Contrast When small symbols, text or detailed graphical representations of information are displayed using colour on a differently coloured back- ground, always ensure luminance contrast with the background.
  34. Beat Signer - Department of Computer Science - bsigner@vub.be 34

    February 18, 2021 Form If large areas are defined using nearly equiluminous colours, consider using thin border lines with large luminance differences (from the colours of the areas) to help define the shapes.
  35. Beat Signer - Department of Computer Science - bsigner@vub.be 35

    February 18, 2021 Colour Contrast Illusion
  36. Beat Signer - Department of Computer Science - bsigner@vub.be 36

    February 18, 2021 Colouring Maps Use low-saturation colours to colour code large areas. Generally, light colours will be best because there is more room in colour space in the high-lightness region than in the low-lightness region.
  37. Beat Signer - Department of Computer Science - bsigner@vub.be 37

    February 18, 2021 Colouring Maps ... When colour coding large background areas overlaid with small coloured symbols, consider using all low-saturation, high-value (pastel) colours for the background, together with high-saturation symbols on the foreground.
  38. Beat Signer - Department of Computer Science - bsigner@vub.be 38

    February 18, 2021 ColorBrewer: Colour Advice for Maps
  39. Beat Signer - Department of Computer Science - bsigner@vub.be 39

    February 18, 2021 Viz Palette
  40. Beat Signer - Department of Computer Science - bsigner@vub.be 40

    February 18, 2021 Exercise 2 ▪ Data Sources and Dataset Quality Assessment
  41. Beat Signer - Department of Computer Science - bsigner@vub.be 41

    February 18, 2021 Further Reading ▪ Major parts of this lecture are based on the book Information Visualization – Perception for Design ▪ chapter 2, chapter 3 and chapter 4
  42. Beat Signer - Department of Computer Science - bsigner@vub.be 42

    February 18, 2021 References ▪ Information Visualization: Perception for Design, Colin Ware, Morgan Kaufmann (3rd edition) May 2012, ISBN-13: 978-0123814647 ▪ ColorBrewer ▪ https://colorbrewer2.org ▪ Viz Palette ▪ https://projects.susielu.com/viz-palette ▪
  43. 2 December 2005 Next Lecture Data Representation