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

1135dc242dcff3b90ae46fc586ff4da8?s=47 Beat Signer
February 18, 2020

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

February 18, 2020
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  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, 2020 Information Visualisation Process Data Representation Data Data Presentation Interaction perception and visual thinking mapping
  3. Beat Signer - Department of Computer Science - bsigner@vub.be 3

    February 18, 2020 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.
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    February 18, 2020 Model of Perceptual Processing "What" system
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    February 18, 2020 Model of Perceptual Processing ...  Stage 1: Parallel processing to extract low-level features  billions of neurons working in parallel (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!
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    February 18, 2020 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.
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    February 18, 2020 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, 2020 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.
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    February 18, 2020 Anatomy of the Human Eye https://commons.wikimedia.org/wiki/File:Blausen_0388_EyeAnatomy_01.png Fovea
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    February 18, 2020 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, 2020 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, 2020 Visual Field Visual field [Information Visualization, Colin Ware, 2013]
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    February 18, 2020 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.
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    February 18, 2020 Visual Acuity  Eyesight = retinal focus + brain interpretation  Extent to which we can perceive details  ability to identify black symbols on a white background at a standardised distance when the size of the symbols is varied  Important for the maximum density of data on a screen
  15. Beat Signer - Department of Computer Science - bsigner@vub.be 15

    February 18, 2020 Brightness  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
  16. Beat Signer - Department of Computer Science - bsigner@vub.be 16

    February 18, 2020 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]
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    February 18, 2020 Simultaneous Brightness Contrast Simultaneous brightness contrast [Information Visualization, Colin Ware, 2013]
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    February 18, 2020 Chevreul Illusion Chevreul illusion [Information Visualization, Colin Ware, 2013]
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    February 18, 2020 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.
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    February 18, 2020 Contrast Effects and Shading  Computer graphics shading algorithms  uniform shading (Chevreul)  Gouraud shading (Mach banding)  Phong shading Shading [Information Visualization, Colin Ware, 2013] uniform (flat) shading Gouraud shading Phong shading
  21. Beat Signer - Department of Computer Science - bsigner@vub.be 21

    February 18, 2020 Edge Enhancement Consider using Cornsweet contours instead of simple lines to define convoluted bounded regions.
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    February 18, 2020 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]
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    February 18, 2020 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]
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    February 18, 2020 Monitor/Projector Setup Ideally, when setting up a monitor for viewing data, a light neutral- colored 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 gray) 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.
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    February 18, 2020 Colour Breaking Camouflage Finding the cherries [Information Visualization, Colin Ware, 2013]
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    February 18, 2020 Cone Cell Sensitivity
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    February 18, 2020 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
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    February 18, 2020 Colour Measurement  C=rR+gG+bB
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    February 18, 2020 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  ...
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    February 18, 2020 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.
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    February 18, 2020 Luminance Contrast When small symbols, text or detailed graphical representations of information are displayed using color on a differently coloured backg- round, always ensure luminance contrast with the background.
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    February 18, 2020 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.
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    February 18, 2020 Colour Contrast Illusion
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    February 18, 2020 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.
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    February 18, 2020 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.
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    February 18, 2020 ColorBrewer: Colour Advice for Maps
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    February 18, 2020 Viz Palette
  38. Beat Signer - Department of Computer Science - bsigner@vub.be 38

    February 18, 2020 Exercise 2  Discussion of Visualisations
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    February 18, 2020 Further Reading  Major parts of this lecture are based on the book Information Visualization - Perception for Design  chapter 2, chapter 3 and chapter 4
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    February 18, 2020 References  Information Visualization: Perception for Design, Colin Ware, Morgan Kaufmann (3rd edition) May 2012, ISBN-13: 978-0123814647 
  41. 2 December 2005 Next Lecture Data Representation