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Estimating natural illumination from a single outdoor scene

Estimating natural illumination from a single outdoor scene. Based on, “Estimating Natural Illumination from a Single Outdoor Image” by Jean-Francois Lalonde , Alexei A. Efros , and Srinivasa G. Narasimhan CVPR ‘09. Sagnik Dhar (107219686) Debaleena Chattopadhay (107224101)

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Estimating natural illumination from a single outdoor scene

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  1. Estimating natural illumination from a single outdoor scene Based on, “Estimating Natural Illumination from a Single Outdoor Image” by Jean-Francois Lalonde, Alexei A. Efros, and Srinivasa G. Narasimhan CVPR ‘09 Sagnik Dhar (107219686) Debaleena Chattopadhay (107224101) CourseProjectfor CSE – 591 Fall ‘09

  2. Estimation of illumination

  3. Why do people care about illumination ? Insertion of objects • Retrieval of objects from a database

  4. Approach Output a Probable Distribution of Sun’s Zenith angle Natural Outdoor Daytime Scene Extract Plausible Cues Sky Cue Vertical Surface Cue Shadow Cue Simulate Normal Distribution of sun positions Perez Modeling of Skies Compute Size Ratio Of Surfaces To The Scene Intensity Of The Vertical Surfaces Weighed By Their Size Vote For Possible Sun Directions Determining Best Linear Shadow Intensity Gradients Vote For Possible Sun positions

  5. Sky Cue • Extract Sky Segment • Determine Horizon Line • Simulate Perez Sky Model to create 360 skies (each for a sun zenith angle) • (Video) • Classify into Clear, Patchy or Overcast category and Process • (KNN) • Taking our sky segment as the mean, find a probability distribution of sun zenith angle

  6. KNN Classification of skies • Sky • Clear • Patchy • Overcast • Discard Cue • Continue Binary segmentation to remove clouds

  7. Shadow Cue • Extract ground Segment Convert to CIE L*a*b Color Space Detect Shadow Pixels in b channel • Detect Longest Linear Shadow Find Intensity orientation of the shadow pixels Cluster Using K-means • Shadow pixel intensities vote for probable sun directions Top Cluster votes for Sun Directions

  8. Vertical Surface Cue • Extract Vertical Surfaces • Compute surface size proportionality with respect to the scene. • Find intensity of each surfaces • Surface size weighted by intensity vote for their preferred Sun direction

  9. Sun Zenith angle

  10. Cue Combination 2ND 3RD 271° Sky 4TH 1ST Behind Vertical Surfaces 14° Shadow

  11. Results- Best Case

  12. Results- Best Case

  13. Results- Best Case 100° Sky 2ND 3RD Right Vertical Surfaces 4TH 1ST 8° Shadow

  14. Results- Best Case

  15. Results- Best Case

  16. Results- Best Case 181° Sky 189° Shadow 2ND 3RD Left Vertical Surfaces 4TH 1ST

  17. Results- Sky + Vertical Cue

  18. Results- Sky + Vertical Cue

  19. Results- Sky + Vertical Cue 2ND 3RD 271° Sky 4TH 1ST Behind Vertical Surfaces

  20. Results- Shadow + Vertical Cue

  21. Results- Shadow + Vertical Cue

  22. Results- Shadow + Vertical Cue 2ND 3RD 4TH 1ST Behind Vertical Surfaces 14° Shadow

  23. Results- Shadow + Vertical Cue

  24. Results- Shadow + Vertical Cue

  25. Results- Shadow + Vertical Cue 2ND 3RD 4TH 1ST Behind Vertical Surfaces 9° Shadow

  26. Results- Failure Cases

  27. Results- Failure Cases

  28. Results- Failure Cases 2ND 3RD 292° Sky 4TH 1ST Behind Vertical Surfaces 5° Shadow

  29. Results- Failure Cases

  30. Results- Failure Cases

  31. Results- Failure Cases 2ND 3RD 4TH 1ST 0° Sky Behind Vertical Surfaces 16° Shadow

  32. Human Validation

  33. Cue Weightage Investigation

  34. Conclusion • Accurate estimation of sun position – Tough problem for human visual system too. • The photographic eye doesn’t bother to capture “informative” cues for visual systems to analyze. • The weak cues are strongly coupled and highly susceptible to incorrect conclusion. • Shadow detection from a single image is still in the need of better algorithms.

  35. Thank You To end, a quote I came across recently, “All I see is the sun; And even when it's hiding behind a smoke of cloud, the shadow tells me all that there is...”

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