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Science Visualization

Science Visualization. Color & Visual Perception II. Visual Perception. It is the ability to interpret the surrounding environment by processing information that is contained in visible light.

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Science Visualization

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  1. Science Visualization Color & Visual Perception II

  2. Visual Perception • It is the ability to interpret the surrounding environment by processing information that is contained in visible light. • The various physiological components involved in vision are referred to collectively as the visual system, and are the focus of much research in psychology, cognitive science, neuroscience, and molecular biology. • The major problem in visual perception is that what people see is not simply a translation of retinal stimuli (i.e., the image on the retina). SciVis 2013 - page 2

  3. Visual Perception • Our visual perception cannot always be trusted. • The components of an object can distort the perception of the complete object. • Our mind is the final arbiter of truth. • Most optical illusions are the result of: • incongruent design elements at opposite ends of parallel lines • influence of background patterns on the overall design • adjustment of our perception at the boundaries of areas of high contrast • afterimages resulting from eye movements or from kinetic displays • inability to interpret the spatial structure of an object from the context provided by the picture. SciVis 2013 - page 3

  4. Some examples • Color perception • One or two red tone type squares ? The red squares are the same color in the upper part and in the lower part of the "X" SciVis 2013 - page 4

  5. Some examples • Color perception • How many colors ? Only three ! SciVis 2013 - page 5

  6. Some examples • Object characteristics perception • Are the diagonal lines parallel ? • Zöllner illusion • The diagonal lines are parallel. SciVis 2013 - page 6

  7. Some examples • Object characteristics perception • Are the rows of black and white squares all parallel ? The vertical zigzag patterns disrupt our horizontal perception. SciVis 2013 - page 7

  8. Some examples • Perspective perception • Are heights of pillars equal ? • The pillars are identical in size. • Our intuition about perspective influences what we see SciVis 2013 - page 8

  9. Some examples • Perspective perception • Are lengths of the diagonal lines AB and BC equal ? SciVis 2013 - page 9

  10. Some examples • Intensity perception • Have squares A and B equivalent colors ? They equal gray color. SciVis 2013 - page 10

  11. Some examples • Illusory Contours • How many triangles are ? • No triangles ! • Only black circles with white sectors. SciVis 2013 - page 11

  12. Some examples • Object Properties • What the size of the center circles? Equal or not ? SciVis 2013 - page 12

  13. Some examples • Motion perception • How many slow rotation gears are ? SciVis 2013 - page 13

  14. Gestalt theory • Gestalt psychologists working primarily in the 1930s and 1940s raised many of the research questions that are studied by vision scientists today. • Gestalt is a German word "Gestalte" that partially translates to "configuration or pattern" along with "whole or emergent structure". • Its “Laws of Organization” has guided the study of how people perceive visual components as organized patterns or wholes, instead of many different parts. • Six main factors that determine how the visual system automatically groups elements into patterns: Proximity, Similarity, Closure, Symmetry, Common Fate (i.e. common motion), and Continuity. SciVis 2013 - page 14

  15. The Eye: Adaption Process • Visual perception from 10-6 lux to 1010 lux • lux = lumen/m2. • Adaptation: • Adaption to the mean luminance • How? • Transition from perception with cones to rods, adaptation of the pupil‘s width, change of the sensitivity of light sensitive cells. SciVis 2013 - page 15

  16. The Eye: Adaption Process • Accommodation: • Adaption to the distance to the fixed object • How? • Adaption of the lens curvature • Accommodation width (dimension unit: Diopter = 1/f) • D = 1/fnear – 1/ffar • Healthy young persons: 12 Diopters (fnear equals 8 cm) • Healthy older persons (> 60 years) 1 Diopters • Loss of 2 Diopters every 10 years SciVis 2013 - page 16

  17. The Eye: Adaption Process • Accommodation: • Adaption to the distance to the fixed object • Result of accommodation: Depth of Field Blur (DOF) SciVis 2013 - page 17

  18. The Eye: Movement •  How people read scenes ? • Many researches analyze continuous registration of eye movement during reading in picture viewing and in visual problem solving. • Example: • The picture shows what may happen during the first 2 sec. of visual inspection.  SciVis 2013 - page 18

  19. The Eye: Movement •  It can also be noted that there are three different types of eye movements: • Vergence movements involve the cooperation of both eyes to allow for an image to fall on the same area of both retinas. This results in a single focused image.  • Saccadic movements is the type of eye movement that is used to rapidly scan a particular scene/image. • Pursuit movement is used to follow objects in motion SciVis 2013 - page 19

  20. The computational approaches • The major problem with the Gestalt laws • They are descriptive not explanatory. • How humans see continuous contours by simply stating that the brain "prefers good continuity“ ?  • Computational models of vision have had more success in explaining visual phenomena and have largely superseded Gestalt theory. • The computational models of visual perception have been developed for Virtual Reality systems • These are closer to real life situation as they account for motion and activities which are prevalent in the real world SciVis 2013 - page 20

  21. The cognitive approaches • In the 1970s David Marr developed a multi-level theory of vision • It analyzed the process of vision at different levels of abstraction. • In order to focus on the understanding of specific problems in vision, he identified three levels of analysis: • The computational level – it addresses, at a high level of abstraction, the problems that the visual system must overcome. • The algorithmic level -  it attempts to identify the strategy that may be used to solve these problems. • The implementation level -  attempts to explain how solutions to these problems are realized in neural circuitry. SciVis 2013 - page 21

  22. The cognitive approaches • Marr described vision as proceeding from a two-dimensional visual array (on the retina) to a three-dimensional description of the world as output. His stages of vision include: • A 2D (primal) sketch of the scene • A 2½ D sketch of the scene • A 3 D model SciVis 2013 - page 22

  23. The cognitive approaches • Marr described vision as proceeding from a two-dimensional visual array (on the retina) to a three-dimensional description of the world as output. His stages of vision include: • A 2D (primal) sketch of the scene – it is based on feature extraction of fundamental components of the scene, including edges, regions, etc. • The similarity in concept to a pencil sketch drawn quickly by an artist as an impression. • A 2½ D sketch of the scene • A 3 D model SciVis 2013 - page 23

  24. The cognitive approaches • Marr described vision as proceeding from a two-dimensional visual array (on the retina) to a three-dimensional description of the world as output. His stages of vision include: • A 2D (primal) sketch of the scene. • A 2½ D sketch of the scene, where textures are acknowledged, etc. • The similarity in concept to the stage in drawing where an artist highlights or shades areas of a scene, to provide depth. • A 3 D model. SciVis 2013 - page 24

  25. The cognitive approaches • Marr described vision as proceeding from a two-dimensional visual array (on the retina) to a three-dimensional description of the world as output. His stages of vision include: • A 2D (primal) sketch of the scene. • A 2½ D sketch of the. • A 3 D model - the scene is visualized in a continuous, 3D map. SciVis 2013 - page 25

  26. A Simple Visual Stimulus • A single uniform dot of luminance Y in a large uniform background of luminance YB. • Question: How much difference is necessary for a “standard observer” to notice the difference between Y and YB? • Two definitions: • The just noticeable difference (JND) is the smallest detectable difference between a starting and secondary level of a particular sensory stimulus. • It is a statistical, rather than an exact quantity • The just noticeable difference (JND) is the difference that allows an observer to detect the center stimulus 50% of the time. • ∆YJND is the difference in Y and YB required to achieve a just noticeable difference. SciVis 2013 - page 26

  27. Weber’s Contrast SciVis 2013 - page 27

  28. Weber’s Contrast SciVis 2013 - page 28

  29. Weber’s Contrast SciVis 2013 - page 29

  30. Weber’s Law SciVis 2013 - page 30

  31. Weber’s Law • The contrast sensitivity is approximately independent of the background luminance. • Relative changes in luminance are important. • Weber’s law tends to break down for very dark and very bright luminance levels. • At very low luminance, detector noise, and ambient light tend to reduce sensitivity, so the stimulus appears “black”.. • At very high luminance, the very bright background tends to saturate detector sensitivity, thereby reducing sensitivity by “blinding” the subject. • We are most concerned with the low and midrange luminance levels. SciVis 2013 - page 31

  32. Perceptually Uniform Representations • Problem: • Unit changes in Luminance Y do not correspond to unit changes in visual sensitivity. • When Y is large, changes luminance are less noticeable ⇒ ∆YJND is large. • When Y is small, changes luminance are more noticeable ⇒ ∆YJND is small. SciVis 2013 - page 32

  33. Consequences from Weber’s law SciVis 2013 - page 33

  34. Contrast Perception SciVis 2013 - page 34

  35. Color Perception • Steps: • Incoming light reaches the eyes. • Light is absorbed by cones. • Light sensation is transmitted to the brain. • In the brain, light is interpreted as color. • Important aspects: • Differentiation between millions of color (TrueColors equals 224 ~ 16 Millions) • At one moment (current adaptation of the pupil) ~ 300 hues and ~ 100 brightness levels may be perceived. SciVis 2013 - page 35

  36. Color Perception • Color perception depends on the visual context and on the viewer. • Color appears less saturated on a dark background compared to a light background. • Color contrast between a foreground object and background has an influence on the perceived size of an object. • Some colors (bright, strongly saturated colors) seem to overlap others. • High frequent data appear less saturated. • Consequence: • Color perception must be evaluated in a specific situation to find out whether the intended effect is actually achieved. SciVis 2013 - page 36

  37. Color Perception • Color appears less saturated on a dark background compared to a light background. • Example: Constant foreground color on a changing background SciVis 2013 - page 37

  38. Color Perception • Color contrast between a foreground object and background has an influence on the perceived size of an object. • Example: Constant background color; varying foreground. SciVis 2013 - page 38

  39. Color Perception • Some colors (bright, strongly saturated colors) seem to overlap others. SciVis 2013 - page 39

  40. Color Perception • High frequent data appear less saturated. SciVis 2013 - page 40

  41. Color Perception • Main principles for the use of color: • Economical, goal-directed use of color • Use strongly saturated colors carefully (prefer pastel colors) • Consider application-specific conventions concerning the meaning of colors (e.g. red – high temperature, blue – very cold) • Distribution of rods and cones based principal: • Do not use pure blue for text, thin lines or small shapes. • Do not use blue for fast moving objects. • To discriminate colors, it is insufficient to change the blue component only slightly. SciVis 2013 - page 41

  42. Motion Perception • Human visual perception is characterized by inertia (persistence of vision). • Frequent (incremental) changes of images are perceived as motion (at least 30-50 frames/second). • Perception is highly sensitive for discontinuities, such as the emergence or disappearance of objects. • Motion is a strong cue to direct attention (e.g. blinking • objects) • Computer-generated visualizations should avoid sudden changes of the direction and/or speed. SciVis 2013 - page 42

  43. Motion Perception • Ability to detect and analyze movements • Essential for traffic (cars, bikers, …) • Essential for sport activities (catch a ball) • Words may be recognized by lip movements • Often combined with depth perception and object recognition, e.g. when catching a ball • Basic facts: • Motion is perceived preattentively. • Up to five different movements can be analyzed. • Speed is considered as a parameter of urgency. • Movements indicate causality, e.g. when a movements starts after another is finished, we usually assume that the first movement triggers the second. SciVis 2013 - page 43

  44. Image Interpretation • It is a part from high-level vision. • High-level vision: • It is about recognizing objects and their relations. • It is about the identification of groups or clusters • Visual perception is influenced by expectations and experiences. • Images, which do not correspond to these expectations are difficult to interpret. A normal glass and smaller liquid or a rotated with bottom up glass? Concave or convex spherical regions? SciVis 2013 - page 44

  45. Image Interpretation: Effectiveness • Visualization of lines is interpreted as a set of overlapping shapes. • The interpretation of both images is the same, but the right is more effective. SciVis 2013 - page 45

  46. Image Interpretation: Special Relations • Support correct spatial perception • Consider expectations • Integrate depth-cues • Shadow projection • Illumination effects • Stereo rendering • Perspective • Attenuation of distant portions (depth of field blur) • Occlusions should be obvious. SciVis 2013 - page 46

  47. Image Interpretation: Special Relations • Shadow projection • Experiment: Users should place a sphere in the center between two spheres and should scale them like the other spheres. • Measurements: Time to accomplish the tasks, errors for different combinations of depth-cues • Result: Shadow projection diminishes the timings and rate of errors strongly. • A slight further improvement is achieved by means of stereo-Rendering. SciVis 2013 - page 47

  48. Image Interpretation: Special Relations • Shadow projection Which ball is closer to us? SciVis 2013 - page 48

  49. Image Interpretation: Special Relations • Illumination effects • Shape recognition is enhanced by lateral light compared to frontal light. • Hard shadows are disturbing. SciVis 2013 - page 49

  50. Image Interpretation: Special Relations • Semi-transparent cursors for Localization in 3D • Experiment: User should select an object (one group with and one group without semi-transparent cursor) • Result: 59 % less errors and 28 % faster with the semi-transp. cursor SciVis 2013 - page 50

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