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VISUAL ANALYTICS AND IMAGING LAB

HUMAN COMPUTATION IN VISUALIZATION: USING PURPOSE DRIVEN GAMES FOR ROBUST EVALUATION OF VISUALIZATION ALGORITHMS. NAFEES AHMED, ZIYI ZHENG, KLAUS MUELLER. VISUAL ANALYTICS AND IMAGING LAB. DEPT OF COMPUTER SCIENCE. HUMAN COMPUTATION Relevance in visualization AN Example: Disguise

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VISUAL ANALYTICS AND IMAGING LAB

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  1. HUMAN COMPUTATIONIN VISUALIZATION:USING PURPOSE DRIVEN GAMESFOR ROBUST EVALUATION OF VISUALIZATION ALGORITHMS NAFEES AHMED, ZIYI ZHENG, KLAUS MUELLER VISUAL ANALYTICS AND IMAGING LAB DEPT OF COMPUTER SCIENCE

  2. HUMAN COMPUTATION • Relevance in visualization • AN Example: Disguise • Findings • Future thoughts

  3. human Computation WHAT & WHY?

  4. ?

  5. ?

  6. ? Crowd sourcing VizWiz Soylent

  7. ? PURPOSE DRIVEN games ESP Game Tag a tune verbosity fold.it Ete-RNA Phylo Eye-wire

  8. HUMAN COMPUTERSin VISUALIZATION WHY DOES IT MAKE SENSE?

  9. VISUALIZATION Creates Understandable and Meaningful pictures from data Human brain and vision In analyzing pictures and patterns still far better than any computers

  10. VISUALIZATION rendering RAWData PreparedData FocusData GeometricData ImageData DataAnalysis Filtering Mapping Rendering VISUALIZATION DESIGN Image Data RAWData Design/SelectCandidate Visualize Data Finalized Method Not Satisfactory Evaluate Satisfactory

  11. Evaluation of visualization • User evaluation • controlled environment • Cost • Small number of samples • Crowd sourced • Noisy data • Cost

  12. evaluation:using an online game Design, implementation and findings

  13. problem Disc A has color Disc B has color When A is on top of B we perceive a blend Transparency perception ability to perceive the ordering given the blended color Evaluate blending algorithm In terms of transparency perception

  14. Algorithms Porter & Duff Hue Preserving Local blending with blurring Local blending [Wang 09] [Porter 84] [Chuang 09]

  15. ARCADE | survival

  16. ARCADE | survival

  17. correctness SCORE SCORE INTRUDER OVER COLLECTOR HITINTRUDER NO ACTION INTRUDER EXPLODES ANY STATE HEALTH HEALTH SCORE SCORE COLLECTOROVER INTRUDER HITINTRUDER CLICK EMPTY SPACE ANY STATE HEALTH HEALTH SCORE ANY STATE HITCOLLECTOR HEALTH

  18. correctness • PLAYERS try to Optimize score • Score only improves with correct guessing of order • Each click gives us feedback about human perception of transparency ordering

  19. observations Playabilitywithin 15 days of launch…261 players30,000+ data-points generated.

  20. observations Data collection speed14.6 clicks per minute1000 players x 24hr playtime= 21 million data points!

  21. observations costonce developed and deployedalmost FREE!

  22. observations Data qualityProvides much less noisy data thanother web-based methods. trying to optimize score, not earning! Extent of data-points collected

  23. observations

  24. observations limitationsTIME TO DEVELOPensuring correctness and funMAKING IT POPULAR

  25. Future thoughts Ideas, motivations

  26. Future thoughts INTEGRATION WITH A LEARNING AGENT FOR ADAPTIVE DESIGN OF NEW ALGORITHMS

  27. Future thoughts OPTIMIZATION OF VISUALIZATION PARAMETERS (transfer functions/ view angles)

  28. http://vail.cewit.stonybrook.edu/Projects/HPU/Disguise THANK you Ziyizhengzizhen@cs.stonybrook.edu www.cs.sunysb.edu/~zizhen/ NAFEES AHMEDnuahmed@cs.stonybrook.eduwww.kuntal.name Klaus muellermueller@cs.stonybrook.edu www.cs.sunysb.edu/~mueller/ VISUAL ANALYTICS AND IMAGING LAB DEPT OF COMPUTER SCIENCE

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