book

Information Visualization, 3rd Edition

by Colin Ware

May 2012

Beginner to intermediate

536 pages

16h 18m

English

Morgan Kaufmann

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Visualization StagesExperimental Semiotics Based on PerceptionSemiotics of GraphicsSensory versus Arbitrary SymbolsGibson's Affordance TheoryA Model of Perceptual ProcessingCosts and Benefits of VisualizationTypes of DataMetadataConclusion
The EnvironmentThe EyeThe Optimal DisplayConclusion
Neurons, Receptive Fields, and Brightness IllusionsLuminance, Brightness, Lightness, and GammaPerception of Surface LightnessMonitor Illumination and Monitor SurroundsConclusion
Trichromacy TheoryColor MeasurementOpponent Process TheoryProperties of Color ChannelsColor AppearanceApplications of Color in VisualizationApplication 1: Color Specification Interfaces and Color SpacesApplication 2: Color for Labeling (Nominal Codes)Application 3: Color Sequences for Data MapsApplication 4: Color ReproductionConclusion

Eye MovementsV1, Channels, and Tuned ReceptorsPreattentive Processing and Ease of SearchIntegral and Separable Dimensions: Glyph DesignRepresenting QuantityThe Searchlight Metaphor and Cortical MagnificationConclusion
Gestalt LawsTexture: Theory and Data MappingPerception of Transparency: Overlapping DataPerceiving Patterns in Multidimensional Discrete DataPattern LearningThe Visual Grammar of Node–Link DiagramsThe Visual Grammar of MapsPatterns in MotionPerception of Animated MotionThe Processes of Pattern Finding
Depth Cue TheoryDepth Cues in CombinationTask-Based Space PerceptionTracing Data Paths in 3D GraphsJudging the Morphology of SurfacesPatterns of Points in 3D SpacePerceiving Patterns in 3D TrajectoriesJudging Relative Positions of Objects in SpaceJudging the Relative Movements of Self within the EnvironmentSelecting and Positioning Objects in 3DJudging the “Up” DirectionThe Aesthetic Impression of 3D Space (Presence)Conclusion
Image-Based Object RecognitionStructure-Based Object RecognitionThe Object Display and Object-Based DiagramsFacesCoding Words and ImagesLabels and ConceptsConcept MappingIconic Images versus Words versus Abstract SymbolsScenes and Scene GistConclusion
The Nature of LanguageIntegrating Visual and Verbal and the Narrative ThreadAnimated versus Static PresentationsVisual NarrativeConclusion
Data Selection and Manipulation LoopExploration and Navigation LoopFocus, Context, and Scale in Nonmetaphoric InterfacesConclusion
The Cognitive SystemMemory and AttentionLong-Term MemoryKnowledge Formation and Creative ThinkingVisualizations and Mental ImagesReview of Visual Cognitive System ComponentsVisual Thinking AlgorithmsAlgorithm 1: Visual QueriesAlgorithm 2: Pathfinding on a Map or DiagramAlgorithm 3: Reasoning with a Hybrid of a Visual Display and Mental ImageryAlgorithm 4: Design SketchingAlgorithm 5: BrushingAlgorithm 6: Small Pattern Comparisons in a Large Information SpaceAlgorithm 7: Degree-of-Relevance HighlightingAlgorithm 8: Generalized Fisheye ViewsAlgorithm 9: Multidimensional Dynamic Queries with Scatter PlotAlgorithm 10: Visual Monitoring StrategiesConclusion

Content preview from Information Visualization, 3rd Edition

Chapter Three

Lightness, Brightness, Contrast, and Constancy

It would be dull to live in a gray world, but we would actually get along just fine 99% of the time. Technically, we can divide color space into one luminance (gray scale) dimension and two chromatic dimensions. It is the luminance dimension that is most basic to perception. Understanding it can help us answer practical questions: How do we map data to a gray scale? How much information can we display per unit area? How much data can we display per unit time? Can gray scales be misleading? (The answer is “yes.”)

To understand the applications of gray scales we need to address other, more fundamental questions: How bright is a patch of light? What is white? What is black? What is a middle ...