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Eye Tracking for Personalized Photography

This research explores the innovative application of eye tracking technology in personalized photography, conducted by UCSC student Steven Scher under the guidance of advisors James Davis and Sriram Swaminarayan from LANL. The study delves into techniques such as automated photo effects, object recognition, and gaze-based interactions to create tailored photographic experiences. The integration of human-computer hybrid computation aims to enhance image editing processes, offering insights into selective defocus and content-aware retargeting. This research paves the way for more intuitive and interactive photography tools.

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Eye Tracking for Personalized Photography

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  1. Eye Tracking for Personalized Photography Steven Scher (UCSC student) James Davis (UCSC advisor) SriramSwaminarayan (LANL advisor)

  2. Who’s Behind the Camera? Photo Creative Commons http://www.flickr.com/photos/jasonpratt

  3. Who’s Behind the Camera? Photo Creative Commons http://www.flickr.com/photos/scobleizer

  4. Human-Computer Hybrid Computation Photo Creative Commons http://www.flickr.com/photos/aloha75 Photo Creative Commons http://www.flickr.com/photos/m500/ Photo Creative Commons http://www.flickr.com/photos/popculturegeek

  5. Human-Computer Hybrid ComputationSemi-automated “Photoshop” effects • Rotoscoping • Keyframe-Based Tracking for Rotoscoping and Animation • Tone Mapping • Interactive local adjustment of tonal values

  6. Human-Computer Hybrid ComputationAmazon Mechanical Turk • Object Recognition as “20 questions” • Visual Recognition with Humans in the Loop • Object Tracking • Efficiently Scaling Up Video Annotation with Crowdsourced Marketplaces

  7. Human-Computer Hybrid ComputationEyetrack-automated “Photoshop” effects • Cropping • Gaze-Based Interaction for Semi-Automatic Photo Cropping

  8. Saliency:What is this a picture of?

  9. Automatic Saliency DetectionHarel et al

  10. Saliency Itti, Koch, &Niebur A Model of Saliency-based Visual Attention for Rapid Scene Analysis Harel, Koch, & Perona Graph Based Visual Saliency Feature extraction Activation Normalization Summation

  11. Saliency:Who is this a picture of?

  12. Eye Tracking Half-Mirror Infrared Light Infrared camera

  13. Future Expectations:EyeTracker in Camera Viewfinder Viewfinder Camera

  14. Future Expectations:EyeTracker in Camera Viewfinder Canon EOS A2 “Eye Controlled Focus” 1992

  15. Photo Creative Commons http://www.flickr.com/photos/1080p/

  16. Eye Tracks (1kHz)

  17. Eye Tracking Eye Tracking Error (pixels) on 800x600 screen Seconds after finishing calibration

  18. Eye Tracks (1kHz)

  19. Gaussian Weighted According to Eyetracker’s Calibration accuracy

  20. Logarithmically Flattened

  21. Automatic Saliency DetectionItti et al

  22. Automatic Saliency DetectionHarel et al

  23. Actual Eyetrack

  24. Automatic Saliency DetectionItti et al

  25. Automatic Saliency DetectionHarel et al

  26. Actual Eyetrack

  27. Applications Selective Defocus Content-Aware Retargeting

  28. Applications Selective Defocus Content-Aware Retargeting

  29. Applications Selective Defocus Content-Aware Retargeting

  30. Applications Selective Defocus Content-Aware Retargeting

  31. Original Image

  32. Selectively Defocused

  33. Selectively Defocused

  34. Selectively Defocused

  35. Selectively Defocused

  36. Applications Selective Defocus Content-Aware Retargeting

  37. Content Aware Retargeting

  38. New Aspect Ratio

  39. Content Aware Retargeting

  40. Seam CarvingRemove one pixel from each row

  41. New Aspect Ratio

  42. Original Image

  43. Simple Resizing

  44. Actual Eyetracks

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