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Northeastern University, Fall 2005 CSG242: Computational Photography

Northeastern University, Fall 2005 CSG242: Computational Photography. Ramesh Raskar Mitsubishi Electric Research Labs Northeastern University Oct 19th, 2005. Course WebPage : http://www.merl.com/people/raskar/photo/course/. Plan for Today. “The Eye As a Camera” Michael Sandberg

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Northeastern University, Fall 2005 CSG242: Computational Photography

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  1. Northeastern University, Fall 2005CSG242: Computational Photography Ramesh Raskar Mitsubishi Electric Research Labs Northeastern University Oct 19th, 2005 Course WebPage : http://www.merl.com/people/raskar/photo/course/

  2. Plan for Today • “The Eye As a Camera” Michael Sandberg • Computational Illumination • Second Programming Assignment • Mid Term • Oct 26th • Project Proposals Due • November 2nd • Paper reading • 2 per student, 15 mins each, Reading list on the web • Starts Nov 2nd

  3. Credits • Assignments: • Five project-oriented assignments • Requires programming in Matlab • 8 points each (Last assignment format is flexible) • Mid-term Exam • 20 points • Paper reading (two papers per student, 15 min presentation, 5pts each) • 10 points (Was Term Paper, 15 points) • Final Project • Individual or in a group of 2 • 20 points • Discretionary credit • 10 points (Was 5)

  4. Tentative Schedule • Oct 26: Midterm exam • Nov 2nd Project Proposals Due • Nov 9th Class ? Likely on 10th • Nov 16th Class ? • Nov 23rd -> Likely on Nov 22nd • Nov 30th • Dec 7th • Dec 15th (Exam week) Projects

  5. Mid-Term • Oct 26th at 6pm, • Duration: 90 minutes • Questions: Think, Explore, Solve • No need to remember all the formulas in detail • More concepts than math problems • Drawing diagrams to explain concepts • 20 points • Topics • All material covered till Oct 19th • Slides, assignments and in-class discussions • Basics, Dynamic Range, Focus, Illumination

  6. Focus

  7. Computational Illumination

  8. Synthetic LightingPaul Haeberli, Jan 1992

  9. Computational Photography Novel Illumination Light Sources Novel Cameras GeneralizedSensor Generalized Optics Processing Display Scene: 8D Ray Modulator Recreate 4D Lightfield

  10. Photography Artifacts: Flash Hotspot Ambient Flash Flash Hotspot

  11. Reflections due to Flash Underexposed Reflections Ambient Flash

  12. Flash Brightness Falloff with Distance Flash Distant people underexposed

  13. Combining Flash/No-flash Images for High Dynamic Range (HDR) Imaging

  14. HDR Scene: Need Both Ambient and Flash!! Ambient Flash Well-lit in Ambient Underexposed Well-lit in Flash

  15. Conventional Exposure HDR:Varying Exposure Time 1/250 1/100 1/20 1/5 1 4 Exposure Time

  16. 42 7 1 0 Flash Brightness Flash HDR:Varying Flash Brightness Scene distance dependence

  17. Flash-Exposure Sampling Flash-Exposure HDR:Varying both Flash Brightness Exposure Time

  18. Capturing HDR Image Varying Exposure time Varying Flash brightness Varying both

  19. Flash Brightness Exposure Time Do We Need All Images ? Next Best Picture ? Flash Brightness Exposure Time Regular 2D Sampling 24 Pictures Adaptive Sampling 5 pictures • Based on all previous pictures • Maximize well-lit pixels over the image • Exclude pixels already captured as well-exposed

  20. Exposure Time Underexposed Still Underexposed Well-exposed ? HDR Image using N images HDR Image using N+1 images ? ? Flash Brightness N+1th picture ?

  21. Non-photorealistic Camera: Depth Edge Detection and Stylized Rendering usingMulti-Flash Imaging Ramesh Raskar, Karhan Tan, Rogerio Feris, Jingyi Yu, Matthew Turk Mitsubishi Electric Research Labs (MERL), Cambridge, MA U of California at Santa Barbara U of North Carolina at Chapel Hill

  22. Car Manuals

  23. What are the problems with ‘real’ photo in conveying information ? Why do we hire artists to draw what can be photographed ?

  24. Shadows Clutter Many Colors Highlight Shape Edges Mark moving parts Basic colors

  25. A New Problem Shadows Clutter Many Colors Highlight Edges Mark moving parts Basic colors

  26. Why Non-photorealistic (NPR) Images ? • Easy to Understand • Easy to Display • Require not-so-rich (3D) data Can we directly capture using a camera ? • Quick comprehensible images for the masses • Tools for the artists

  27. Depth Edge Camera

  28. Depth Discontinuities Internal and externalShape boundaries, Occluding contour, Silhouettes

  29. Depth Edges

  30. Sigma = 9 Sigma = 5 Canny Intensity Edge Detection Sigma = 1 Our method captures shape edges

  31. Canny Our Method

  32. Photo Our Method

  33. Result Photo Canny Intensity Edge Detection Our Method

  34. Our Method Canny Intensity Edge Detection

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