Estimating natural illumination from a single outdoor scene -- Project Presentation

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

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

35. The photographic eye doesn’t bother to capture “informative” cues for visual systems to analyze.

36. The weak cues are strongly coupled and highly susceptible to incorrect conclusion.