Perception - PowerPoint PPT Presentation

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Perception

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Gabor detector tuned to specific orientation and size information in space. Orientation or size can be specified exactly, but not both, hence the trade-off. ... – PowerPoint PPT presentation

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Title: Perception


1
Perception
  • Visual Attention and Information That Pops Out
  • Scales of Measurement

2
  • Scales of Measurement
  • Eye Movement
  • Visual Attention, Searching, and System
    Monitoring
  • Reading From the Iconic Buffer
  • Neural Processing, Graphemes and Tuned Receptors
  • The Gabor Model and Texture In Visualization
  • Texture Coding Information
  • Glyphs and Multivariate Discrete Data

3
Scales Of MeasurementOn the Theory of
Measurement, S.S. Stevens, Science, 103,
pp.677-680. 1946
  • Nominal
  • Ordinal
  • Interval
  • Ratio

4
Nominal
  • name only, arbitrary, any one-to-one substitution
    allowed
  • words or letters would serve as well as numbers
  • stats number of cases, mode, contingency
    correlation
  • e.g numbers on sports team, names of classes

5
Ordinal
  • rank-ordering, order-preserving
  • intervals are not assumed equal
  • most measurements in Psychology use this scale
  • monotonic increasing functions
  • stats median, percentiles
  • e.g. hardness of minerals, personality traits

6
Interval
  • quantitative, intervals are equal
  • no true zero point, therefore no ratios
  • Psychology aims for this scale
  • general linear group
  • stats mean, standard deviation, rank-order
    correlation, product moment correlation
  • e.g. Centigrade, Fahrenheit, calendar days

7
Ratio
  • determination of equality of ratios (true zero)
  • commonly seen in physics
  • stats coefficient of variation
  • fundamental (additivity e.g. weights)
  • derived (functions of above e.g. density, force)

8
Eye Movements
  • Saccadic Movement
  • fixation point to fixation point
  • dwell period 200-600 msec
  • saccade 20-100 msec
  • Smooth Pursuit Movement
  • tracking moving objects in visual field
  • Convergent Movement
  • tracking objects moving away or toward us

9
  • Saccadic suppression
  • the decrease in sensitivity to visual input
    during saccadic eye movement
  • Brain often processing rapid sequences of
    discrete images
  • Accommodation
  • refocusing when moving to a new target at
    different distances
  • neurologically coupled with convergent eye
    movement

10
Visual Attention, Searching, and System Monitoring
  • Our visual attention is usually directed at what
    we are currently fixating on.
  • Supervisory Control
  • complex semiautonomous systems, only indirectly
    controlled by human operators
  • uses searchlight metaphor

11
  • Human-Interrupt Signal
  • effective ways of computer to gain attention
  • warning
  • routine change of status
  • patterns of events
  • Visual Scanning Strategies
  • Elements
  • Channels, Events, Expected Costs
  • Factors
  • minimizing eye movement, over-sampling of
    channels, dysfunctional behaviours, systematic
    scan patterns

12
  • Useful Field of View (UFOV)
  • expands searchlight metaphor
  • size of region from which we can rapidly take
    information
  • maintains constant number of targets
  • Tunnel Vision and Stress
  • UFOV narrows as cognitive load/stress goes up
  • Role of Motion in Attracting Attention
  • UFOV larger for movement detection

13
4 Requirements of User Interrupt
  • easily perceived signal, even when outside of
    area of attention
  • continuously reminds user if ignored
  • not too irritating
  • signal conveys varying levels of urgency

14
How to attract users attention problems
  • Difficult to detect small targets in periphery of
    visual field.
  • Colour blind in periphery (rods).
  • Saccadic suppression allows for the possibility
    of transitory events being missed.

15
Movement possible solution
  • Seen in periphery.
  • Research supports effectiveness of motion.
  • Urgency can be effectively coded using motion.
  • Appearance of new object attracts attention more
    than motion alone.

16
Reading from the Iconic Buffer
  • Iconic Buffer
  • short-lived visual buffer holds images for 1-2
    seconds prior to transfer to short-term/working
    memory
  • Pre-attentive Processing
  • theoretical mechanism underlying pop-out
  • occurs prior to conscious attention
  • Following examples from Joanna McGreneres HCI
    class slides.

17
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18
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19
Pop Out
  • Time taken to find target independent of number
    of distracters.
  • Possible indication of primitive features
    extracted early in visual processing.
  • Less distinct as variety of distracters
    increases.
  • Salience depends on strength of particular
    feature and context.

20
Pop Out Examples
  • Form
  • line orientation, length, width
  • spatial orientation, added marks, numerosity (4)
  • Colour
  • hue, intensity
  • Motion
  • flicker, direction of motion
  • Spatial Position
  • stereoscopic depth, convex/concave shape

21
Color
22
Orientation
23
Motion
24
Simple shading
25
(No Transcript)
26
  • Rapid Area Judgement
  • fast area estimation done on basis of colour or
    orientations of graphical element filling a
    spatial region
  • Conjunction Search
  • combination of features not generally
    pre-attentive
  • spatially coded information (position on XY
    plane, stereoscopic depth, shape from shading)
    and second attribute (colour, shape) DO allow
    conjunction search

27
Neural Processing, Graphemes, and Tuned Receptors
  • Cells in Visual Areas 1 and 2 differently tuned
    to
  • orientation and size (with luminance)
  • colour (two types of signal)
  • stereoscopic depth
  • motion
  • Massively parallel system with tuned filters for
    each point in visual field.

28
Vision Pathwayhttp//www.geocities.com/ocular_tim
es/vpath2.html
  • Signal leaves retina, passes up optic nerve,
    through neural junction at geniculate nucleus
    (LGN), on to cortex.
  • First areas are Visual Area 1 and Visual Area 2
    these areas have neurons with preferred
    orientation and size sensitivity (not sensitive
    to colour)

29
http//www.geocities.com/ocular_times/vpath.html
30
http//www.geocities.com/ocular_times/vpath.html
31
http//nba5.med.uth.tmc.edu/academic/neuroscience/
lectures/section_2/lecture34_04.htm
32
http//nba5.med.uth.tmc.edu/academic/neuroscience/
lectures/section_2/lecture34_04.htm
33
Grapheme
  • Smallest primitive elements in visual processing,
    analogous to phonemes.
  • Corresponds to pattern that the neuron is tuned
    to detect (filter).
  • Assumption rate of neuron firing key coding
    variable in human perception.

34
Gabor Model and Texture in Visualization
  • Mathematical model used to describe receptive
    field properties of the neurons of visual area 1
    and 2.
  • Explains things in low-level perception
  • detection of contours at object boundaries
  • detection of regions with different visual
    textures
  • stereoscopic vision
  • motion perception

35
Gabor Function
  • Response C cos(Ox/S)exp(-(x² y²)/S)
  • C amplitude, or contrast value
  • S overall size of Gabor function
  • O rotation matrix that orients cosine wave
  • orientation, size, and contrast are most
    significant in modeling human visual processing

36
  • Gabor model helps us understand how the visual
    system segments the visual world into different
    textual regions.
  • Regions are divided according to predominant
    spatial frequency(grain or coarseness of a
    region) and
    orientation information
  • Regions of an image are analyzed simultaneously
    with Gabor filters, texture boundaries are
    detected when best-fit filters for one area are
    substantially different from a neighbouring area.

37
Trade-Offs in Information Density
  • The second dogma (Barlow, 1972)
  • visual system is simultaneously optimized in both
    spatial-location and spatial-frequency domains
  • Gabor detector tuned to specific orientation and
    size information in space.
  • Orientation or size can be specified exactly, but
    not both, hence the trade-off.

38
Texture Coding Information
  • Gabor model can be used to produce easily
    distinguished textures for information display
    (used to represent continuous data).
  • Human neural receptive fields couple the gaussian
    and cosine components, resulting in three
    parameter model
  • O orientation
  • S scale / size
  • C contrast / amplitude

39
  • Textons
  • combinations of features making up small
    graphical shapes
  • Perceptual Independence
  • independence of different sources of information,
    increase in one does not effect how the other
    appears
  • Orthogonality
  • channels that are independent are orthogonal
  • textures differing in orientation by /- 30
    degrees are easily distinguishable

40
Texture Resolution
  • Resolvable size difference of a Gabor pattern is
    9.
  • Resolvable orientation difference is 5.
  • Higher sensitivity due to higher-level
    mechanisms.
  • No agreement on what makes up important higher
    order perceptual dimensions of texture
    (randomness is one example).

41
Glyphs and Multivariate Discrete Data
  • Multivariate Discrete Data
  • data objects with a number of attributes that can
    take different discrete values
  • Glyph
  • single graphical object that represents a
    multivariate data object

42
  • Integral dimensions
  • two or more attributes of an object are perceived
    holistically (e.g.width and height of rectangle).
  • Separable dimensions
  • judged separately, or through analytic processing
    (e.g. diameter and colour of ball).

43
  • Restricted Classification Tasks
  • Subjects asked to group 2 of 3 glyphs together to
    test integral vs. separable dimensions.
  • Speeded Classification Tasks
  • Subjects asked to rapidly classify glyphs
    according to only one of the visual attributes to
    test for interference.
  • Integral-Separable Dimension Pairs
  • continuum of pairs of features that differ in the
    extent of the integral-separable quality
  • integral(x/y size)separable(location/colour)

44
Multidimensional Discrete Data
  • Using glyph display, a decision must be made on
    the mapping of the data dimension to the
    graphical attribute of the glyph.
  • Many display dimensions are not independent (8 is
    probably maximum).
  • Limited number of resolvable steps on each
    dimension (e.g. 4 size steps, 8 colours..).
  • About 32 rapidly distinguishable alternatives,
    given limitations of conjunction searches.

45
Conclusion
  • What is currently known about visual processing
    can be very helpful in information visualization.
  • Understanding low-level mechanisms of the visual
    processing system and using that knowledge can
    result in improved displays.
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