1. Outdoor Image Processing 1

2. Photometric stereo for outdoor webcams  "Photometric stereo for outdoor webcams" Ackermann, J.; Langguth, F.; Fuhrmann, S.; Goesele, M.;, CVPR 2012 Overview:  Photometric stereo from time lapse video captured over a long time span.  Retrieves  Surface Normals  Basic Materials  Material Mixtures  Indirect light 2

3. Assumptions  GPS location of the camera, object and sky mask, per image time stamp are available 3

4. 4

5. Selecting Subsets of images  Image Filtering: 1) Discard images with 10% of the image or the object is overexposed 2) Select only daytime images, zenith < 85 degrees 3) Discard bad weather images – select only top 50% according to score: S I = I sky + I Obj I sky = median of sky pixel intensities I Obj = 75 th percentile of object pixel intensities 5

6. Selecting Subsets of images  Two Image subsets required; 1) Clear sky images for camera calibration, 2) Images with good weathers and well illuminated object for photometric stereo Iteratively select required number of images by updating penalty using a 2D Gaussian function and selecting the best image at that iteration 6

7. Obtaining light direction  Image Alignment: Align gradient images to the average gradient  Camera Calibration: 1) Radiometric response obtained using Kim et al. ( uses pixels under the same lighting conditions to solve for the response function). 2) Absolute zenith, azimuth of the Sun obtained using cam location, timestamp. 3) Use the sky as calibration target ( Lalonde et al.) to find camera zenith, azimuth  Shadow Detection  I max,p / I min,p always shadowed  Otherwise, I i,p shadowed in I i End of Stage 1 ( obtain subset of images with light direction) 7

8. Photometric Stereo stage  Intensity of image i at pixel p and channel c, I i,p,c = I sun,i,p,c + I sky,I,p,c  Reflectance at a pixel is linear combination of basis materials, f m,c Sun light model Intensity of the sun Material mixing coeff Surface normal Sun direction Portion of sky visible at p 8

9. Light Model  Sky light model Assume Finally, Optimize for l i,c, f m,c, n p, γ p,m. V p is replaced by using images I p s.t. pixel p is not in shadow 9

10. Initialization  Set S p,c to zero, assume constant light intensities and Lambertian scene 1. Obtain initial estimates for surface normals and albedo 2. Use these to find initial estimates of the light intensities 3. Cluster the albedos to get an initialization of material properties at each pixel 1) Surface normal and albedo  Solve for classical photometric stereo 10

11. Initialization  2) Relative light intensities:  Need six surface points with similar albedo and differing normal  1) Cluster albedos in 4 groups  2) Cluster normals for pixels with the most frequent albedo  3) Pick normal from different clusters  3) Initial material estimation:  Cluster albedos in sRGB  Identify pure pixel sets for each of the fundamental materials  Solve for the BRDF parameters 11

12. Iterative Refinement  Intensity estimate: updated in each following step 1) Material Fitting  Find optimal parameters for all materials simultaneously, not for only pure pixels  Minimize 12

13. Iterative Refinement  Light intensity optimization:  Minimize,  Material and normal map optimization:  Minimize  Material parameters, light intensities are fixed, only normals, sky light at each pixel, and material mixing coeff are optimized 13

14. Shadow Detection and Removal  Single-Image Shadow Detection and Removal using Paired Regions Ruiqi Guo, Qieyun Dai, Derek Hoiem. CVPR 2012  Employs a region based approach.  Perform pairwise classification (of illumination conditions) of regions based on appearance.  Graph cut is used for the labeling.  Soft matting for refinement  Shadow free image is obtained by relighting pixels under shadow 14

15. Shadow Detection  Maximize  c i shadow – single region classifier confidence * region area  c ij diff, c ij diff – pairwise classifier confidence * f(region areas)  y – shadow labels for regions 15

16. Shadow Detection  Single region classifier (with χ 2 kernel) features 1. Color histograms 2. Texton histograms  Internal appearance of a given region is not enough Comparison between regions of same material needed Pairwise region classifier (RBF kernel) features 1. Χ 2 distance between color and texton histograms 2. Ratios of RGB average intensity ( ρ r = R avg1 /R avg2, …) 3. Chromatic alignment (ρ r /ρ g ) 4. Normalized distances between the regions 16

17. Pairwise region graph 17  Different illumination  black-white  Same-illumination  Green  Not related  Orange

18. Pairwise region graph 18

19. Apply Graph cut  Reformulate the cost function to apply Graph cut 19

20. Shadow Removal Soft shadow Solution: Shadow matting Hard shadow maskSoft shadow matt 20 Slide from Guo et al.

21. Shadow Removal  Simple light model: direct light + env. Light  Relighting: Estimate how much direct light is occluded at each pixel and light up by that amount 1. Find the fractional shadow coefficients using matting technique 2. Find ratio of direct to environment light. 21 Direct light Environmental light Surface Reflectance Shadow coefficient content from from Guo et al.

22. Light Model 22 Non-shadow: t i =1 Umbra: t i =0 Penumbra = 0 < t i < 1 Relighting : I i shadow-free = (L d cosθ i + L e )R i Figure from from Guo et al.

23. Shadow model as a matting problem I i = γ i F i + (1- γ i )B i F: Foreground image, B: background Image 23 Rewrite shadow model as: I i = k i (L d R i + L e R i ) + (1-k i )L e R i Similar to matting eqn. Solve for matting: minimize E(k) = k T L k + λ(k-k’) T D(k-k’) T optimal k obtained by solving sparse system: ( L + λD)k = λdk’

24. Finding the light ratio 24 Final r obtained by voting content from from Guo et al.

25. Results 25  UCF Dataset (Zhu et al.)  245 images  outdoor scenes, manual annotations content from from Guo et al.

26. Experiments: Datasets  UCF Dataset (Zhu et al.)  245 images  outdoor scenes, manual annotations content from from Guo et al.

27. Experiments: Datasets  New UIUC Shadow Dataset  108 images, indoor/outdoor, automatic annotation  Evaluate both shadow detection and removal Input image Groundtruth Nonshadow Shadow mask content from from Guo et al.

28. Results on UCF Dataset Input image Groundtruth Shadow mask DetectionRemoval result content from from Guo et al.

29. Results on UIUC Dataset Input image Detection Removal resultGroundtruth content from from Guo et al.

30. Results: Shadow Detection AccuracyUCF datasetUIUC dataset (ours) Full model0.9000.883 Single region0.8750.796 Zhu et al.0.887- Pixel accuracy content from from Guo et al.

31. Shadow Non-shadow Shadow (GT)0.5150.485 Non- shadow(GT) 0.0570.947 Results: Shadow Detection Shadow Non-shadow Shadow (GT)0.7500.250 Non- shadow(GT) 0.0700.930 Confusion matrices on UCF dataset full modelSingle region classification content from from Guo et al.

32. Shadow Non-shadow Shadow (GT)0.6390.361 Non- shadow(GT) 0.0670.934 Results: Shadow Detection Shadow Non-shadow Shadow (GT)0.7500.250 Non- shadow(GT) 0.0700.930 Confusion matrices on UCF dataset Zhu et al. 2010 full model content from from Guo et al.

33. Failure Example Input imageDetectionRemoval result content from from Guo et al.

34. End 34