Visual Perception. Basics
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- Maryann Ellis
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1 Visual Perception Basics Please refer to Colin Ware s s Book Some materials are from Profs. Colin Ware, University of New Hampshire Klaus Mueller, SUNY Stony Brook Jürgen Döllner, University of Potsdam Barna Reskó, Hungary Academy of Sciences Christopher G. Healey, North Carolina State Univ.
2 Human Eye Scenes projected on the retina through lens Receptors: rod and cones Optic nerves connect to the brain
3 Cones: Retina Receptors Small receptive field (0.01mm) High resolution Slow signal sensing Non-sensitive to motion Color sensing Rods Large receptive field (0.5mm) Low resolution No color perception Fast signal propagation
4 Understanding color and monochrome sensing is separated No shape and direction feature are extracted on retina Shape, direction and other features are extracted and combined with color information by the visual cortex inside brain
5 Contrast Eyes can only operate on small range of intensity difference Sense of contrast needs adaption for different lighting conditions You will need some time to adapt to a new lighting environment such as stepping into a dark or bright room Local adaption of contrast see Colin Ware s book
6 Time Effects of Contrast Look at the black circle for a little while Switch to a white field You will see a white circle Due to the change of adaption with time effects
7 Spatial Effects of Contrast Surrounding properties are important, especially colors The effects can be removed by using neutral borders inside saturated colors
8 Some Interesting Tricks
9 Preattentive Processing (PAP) Certain information in the display is processed in parallel by the low-level visual system.
10 Preattentive Processing (PAP) Discovery of a limited set of visual properties that are detected very rapidly and accurately by the lowlevel visual system Searching by hue
11 PAP Example searching for a target red circle based on a difference in curvature
12 Non PAP Example Detect a red circle in a group of blue circles and red squares objects made up of a conjunction of unique features cannot be detected pre-attentively
13 PAP Interface Detection (a) constant shape, hue boundary can be easily found (b) shape varies, hue boundary can be easily detected (c) hue varies, hard to identify boundary (d) constant hue, shape boundary easily detected
14 Benefits of PAP Using these features for rapid and effective visualization Can use some experiments to identify these features on multiple displays or large systems Target detection Boundary detection Important for design visualization systems
15 Eye and Lens
16 Color Human eyes can only see partial spectrum of light waves Different colors have different focus distances for human eyes Red is more obvious than blue Avoid using pure blue in visualization
17 Static cues Size Depth Perception Cues Absolute size Relative size Perspective Linear perspective Texture gradient Aerial-perspective Shadow
18 3D Depth Cues 3D visualization should provide depth cues for understanding Linear perpective Objects far away from viewer should be smaller Shadows Show relative heights of objects Track object motions Occlusion Important to represent order of depth or distance
19 Depth Perception Cues Dynamic cues Motion parallax: movement of eyes This flash is from Dr. John Krantz, Hanover College
20 Shading Cues Very important in 3D visualization
21 Texture Perception Pre-attentive cues with textures Visual textures of object give PAP visual effects A powerful visualization method Textures: patterns, structures, color Classification of textures Coarseness, contrast, orientation A research topic in many applications Improve perception greatly
22 Texture Perception Textures without information should be avoided
23 Texture Perception Examples of a natural brick texture applied to an underlying 3D object, oriented to follow different properties of the surface at a per-pixel level: (a) orientation follows a default "up" direction; (b) orientation follows the first principle direction; (c) orientation follows the second principle direction; all images courtesy of Victoria Interrante
24 Perception in Visualization Historical weather conditions over the eastern United States for March, colour mapped to temperature (blue and green for cold to red and pink for hot), luminance mapped to wind speed (brighter for stronger winds), orientation mapped to precipitation (more tilted for heavier rainfall), size mapped to cloud coverage (larger for more cloudy), frost frequency mapped to density (denser for higher frost): (a) a nonphotorealistic visualization using simulated brush strokes to display the underlying data; (b) a traditional visualization of the same data using triangular glyphs
25 Perception in Visualization Nonphotorealistic volume illustration Examples of nonphotorealistic enhancements for volume illustration: (a) original greyscale image of an abdominal CT scan; (b) the same image with tone enhancement applied; (c) with volumetric boundary sketching; (d) original colour image of the same abdominal CT scan; (e) with halos and boundary and silhouette enhancement; (f) with tone shading and boundary and silhouette enhancement; all images courtesy of Penny Rheingans
26 Perception in Visualization An example of a perceptually-motivated multidimensional visualization of recent U.S. election results. Color represents party (blue for Democrat, red for Republican, green for Independent), and saturation represents the winning percentage (more saturated for higher percentages); the small disc floating over each state shows aggregated state-wide results; the height of a state represents the number of electoral college votes it controls
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