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Perception for visualization: From design to evaluation

This presentation explores the intersection of perception and visualization, emphasizing the importance of designing information to enhance human comprehension. It discusses the growing complexity of data and the need for effective analysis. The session highlights the challenges faced in understanding perception and provides examples of perceptually-based representations across various disciplines. By harnessing perceptual processes, such as color and motion, presenters can create engaging, informative experiences that prioritize clarity and understanding over mere aesthetics.

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Perception for visualization: From design to evaluation

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  1. Perception for visualization: From design to evaluation Victoria Interrante, U. Minn. Haim Levkowitz (chair), IVPR, UMass Lowell Hans-Peter Meinzer, DKFZ-Heidelberg, Germany

  2. Overview • Purpose of collecting information • Constantly growing ... • Need to understand how to ... • Challenges … • Examples ...

  3. Purpose of collecting information • Present it to • Analyze it by • Human beings • Constantly growing: amounts & complexity ...

  4. Constantly growing ... • Amounts collected • Complexity • Information • Analysis • Need to understand how to ...

  5. Need to understand how to ... • Increase comprehension • Make analysis easier & efficient

  6. A presentation … • An experience, not just an image ... • Aimed at humans • Has to be designed ...

  7. An experience, not just an image ... • User-directed, dynamic • Multidisciplinary … • Multisensory ...

  8. Multidisciplinary ... • Graphics • Imaging • Psychology • Man-machine interface • Databases • Multimedia

  9. Multisensory ... • Visual • Auditory • Tactile • More?

  10. Has to be designed ... • Audience • Question(s) / tasks ... • Contents • Delivery

  11. Question(s) / tasks ... • Explore vs. Confirm • E.g., detection vs. identification

  12. Perceptually-based presentation … • Make perceptually effective … • How? ...

  13. Make perceptually effective … • Understand perceptual processes … • Harness them ...

  14. Understand perceptual processes ... • Color • Size • Shape • Texture • Motion • Sound

  15. Harness them ... • Data "Speak" for themselves • Exploratory vs. confirmatory analyses • Raise dimensionality • MDMV

  16. How? ... • Substitute pre-conscious for conscious • Pre-conscious … • Conscious ...

  17. Pre-conscious ... • Do not interfere with ability to think • Parallel • Hard-wired/entrained • Fast • Relentless • Simultaneously w/conscious analysis

  18. Conscious ... • Interfere • Serial • Ad-hoc • Slow • Cause fatigue • Distract from higher level analysis

  19. Challenges … • Understand "perception" ... • Apply perception ... • Truth and accuracy obligation … • Effectiveness • Evaluation and verification ...

  20. Understand "perception" ... • Lower-level vision (also audition, touch) • Perception … • Cognition … • Aesthetics • Emotional & cultural

  21. Perception ... • Color • Shape • Size • Texture • Motion • Segmentation

  22. Cognition ... • Attention (pre-attentive vs. scrutiny; preconscious vs. conscious) • Memory (iconic, short term, long term) • Semantics & symbolism

  23. Apply perception ... • ==> Perceptually-based • Representations … • Rules & guidelines … • Interaction • ==> Presentations

  24. Representations ... • E.g., generalize pixels fi more info • Visual • Color • Geometry … • Motion • Sound

  25. Geometry ... • Line orientation • Area • Volume • Curvature • Texture

  26. Rules & guidelines ... • Contrast • Color • Font • Size • Style

  27. Truth and accuracy obligation ... • Informing vs. entertaining • Comprehension vs. aesthetics • Informing ==> as truthful as possible • Avoid misleading viewers • E.g., Brooks (Vis '93): background music ...

  28. E.g., Brooks (Vis '93): background music ... • What message? • Lower SNR, unless • Intentional • Accurate • Show business

  29. Evaluation and verification … • Tasks … • Data ...

  30. Tasks ... • Presence / absence • Edge / boundary • Target / blob • Location • Classification

  31. Data ... • Real … • Synthesized ...

  32. Real ... • "Truth" unknown • E.g., • Satellite • Water vs. land • Medical • Normal vs. abnormal

  33. Synthesized ... • "Truth" known • Statistics • Not real

  34. Examples ... • Medical • Earth science • Reconnaissance • Presentations

  35. Conclusions ... • What is information presentation • Perceptually-based information presentation • Design • Evaluation & verification • Challenges • Examples

  36. Moral ... • Present to inform, not to impress; if you inform, you will impress after Fred Brooks Keynote Address Visualization ’93

  37. Emerging discipline • Eick: "Graphically displaying text" • J. Comp. & Graphical Statistics • Four progress stages … • Wong & Bergeron, 1995: "30 years of MDMV Vis” • Four stages of MDMV Vis. Development ...

  38. Four progress stages ... • Skilled artisans: Craft • Practical experience guidelines • Researchers: Scientific principles & theories • Engineers: Production rules • Widely available technology

  39. Four stages of MDMV Vis. Development ... • Pre-1976: The Searching Stage • 1977-1985: The Awakening Stage • 1986-1991: The Discovery Stage • 1992-present: The Elaboration Stage

  40. Tutorial overview • Levkowitz: Vision & color; evaluation and verification • Interrante: Visual perception; cognitive issues • Meinzer: Perception in image generation and understanding • Summary, discussions, future directions

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