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Structure and Aesthetics in Non-Photorealistic Images

Structure and Aesthetics in Non-Photorealistic Images. Hua Li, David Mould, and Jim Davies Carleton University. Artistic or Messed. 2 /35. Related Work on Evaluating Non-Photorealistic Algorithms. Performance based on processing speed ill-suited for stylization

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Structure and Aesthetics in Non-Photorealistic Images

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  1. Structure and Aesthetics in Non-Photorealistic Images Hua Li, David Mould, and Jim Davies Carleton University

  2. Artistic or Messed 2/35

  3. Related Work on Evaluating Non-Photorealistic Algorithms • Performance based on processing speed • ill-suited for stylization • Side-by-side comparisons • not fully convinced by audience 3/35

  4. Perceptual Evaluation on Non-Photorealistic Algorithms • Quantitative evaluation • rating scores [Schumann et al. 96, Gooch and Willemsen 02, Mandryk et al. 2011, Mould et al. 2012] • response time [Gooch et al. 04] • eye-tracking data [Mandryk et al. 2011, Mould et al. 2012] • Qualitative evaluation • questionnaire-based 4/35

  5. Motivation of Our StudyTone  Structure [Floyd and Steinberg 76] [Ostromoukhov 01] [Qu et al. 08] [Ours 11] 5/35

  6. Questions to Answer • Are structural and aesthetic quality related? • Do images matter for side-by-side comparisons? 6/35

  7. Participants • 30 participants • 15 female and 15 male • 11 artists • aged 18 to 33 7/35

  8. Study Overview • 1 ~ 1.5 hours to complete the experiment • Using the keyboard or the mouse to enter their responses • Tasks: • rating structural and aesthetic quality • collecting response times for rendered images 8/35

  9. Image Stimuli • Seven categories • include cars, cats, persons, flowers, buildings, mugs, and birds. • Each category contains 13 different images including • one unprocessed image • and 12 rendered images using 12 algorithms. • Images are black and white, or greyscale to remove the influence of color. 9/36

  10. Procedure • Step 1: verbal introduction • Step 2: training • Step 3: formal study • Step 4: questionnaire • Step 5: ranking 10/35

  11. Interfaces Used 11/35 Interface for collecting the response time

  12. Interfaces Used Aesthetic rating Structural rating 12/35

  13. Experimental Images -Bird Category Unprocessed 13/35

  14. Experimental Images - Bird Category Structure-Aware Structure-Preserving Stippling (SPS) 14/35

  15. Experimental Images - Bird Category Structure-Aware Content-Sensitive Screening (CSS) 15/35

  16. Experimental Images - Bird Category Structure-Aware SPS with Exclusion Masks (SPH) 16/35

  17. Experimental Images - Bird Category Structure-Aware Line Art using edge tangent field (ETF) 17/35

  18. Experimental Images - Bird Category Structure-Aware Artistic Tessellation (AT) 18/35

  19. Experimental Images - Bird Category Structure-Aware Line Art from SPS (Drawing) 19/35

  20. Experimental Images - Bird Category Tone-based Secord’s Stippling Method (Secord) 20/35

  21. Experimental Images - Bird Category Tone-based Line Art using edge tangent field (Mmosaics) 21/35

  22. Experimental Images - Bird Category Contrast-Aware Halftoning (CAH) 22/35

  23. Experimental Images - Bird Category Black and White (BW) 23/35

  24. Experimental Images - Bird Category Reduced Information Adding 50% salt and pepper noise (Noisy) 24/35

  25. Experimental Images - Bird Category Reduced Information Gaussian filter (Blurring) 25/35

  26. Positive Correlation Between Structural and Aesthetic Ratings 26/35

  27. Dot-based Methods (Stippling) Tone-based Structure-Aware Structure-Aware Tone-based 27/35

  28. Region-based Methods (Mosaics) Tone-based Structure-Aware Structure-Aware Tone-based 28/35

  29. Effect of Category on Ratings 29/35

  30. Effect of Category on Response Time Building < Flower < Bird < Cat < Person < Mug < Car 30/35

  31. Artists and Non-Artists 31/35

  32. Overall Ranks after Study • Participants preferred the AT images (7/30 responses) the most, CAH second (6/30). • Participants’ least favorite • blurred images most often (20/30 responses), and with AT second (5/30). • Controversial ranking for stylized images rendered by the AT method. 32/35

  33. Conclusions • Considering structure as a possible way to increase aesthetic appeal. • Considering the choice of the images used. • Generally, bird images were the easiest images to abstract, while Person images were the most difficult. 33/35

  34. Future Work • More Participants • More Categories • More NPR Algorithms • Eye tracker 34/35

  35. Thanks for Your Attention. Questions? 35/35

  36. Effect of Algorithms on Ratings (skip) 36/41

  37. Effect of Algorithms on Response Time (skip) 37/41

  38. Interaction between Categories and Algorithms (skip) Aesthetic scores 38/41

  39. Interaction between Categories and Algorithms (skip) 39/41

  40. Interaction between Categories and Algorithms (skip) 40/41

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