outdoor image processing
DESCRIPTION
Outdoor Image Processing. 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 - PowerPoint PPT PresentationTRANSCRIPT
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Outdoor Image Processing
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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
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Assumptions
GPS location of the camera, object and sky mask, per image time stamp are available
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Selecting Subsets of images
Image Filtering:1) Discard images with 10% of the image or the object is overexposed2) Select only daytime images, zenith < 85 degrees3) Discard bad weather images – select only top 50% according to score: SI= Isky + IObj
Isky = median of sky pixel intensities IObj = 75th percentile of object pixel intensities
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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
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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 Imax,p / Imin,p < 1.4 => always shadowed Otherwise, Ii,p < 1.5*median10% darkest pixels => shadowed in Ii End of Stage 1 ( obtain subset of images with light direction)
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Photometric Stereo stage
Intensity of image i at pixel p and channel c, Ii,p,c = Isun,i,p,c + Isky,I,p,c
Reflectance at a pixel is linear combination of basis materials, fm,c
Sun light model
Intensity of the sun
Material mixing coeff
Surface normal
Sun direction
Portion of sky visible at p
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Light Model
Sky light model
Assume Finally,
Optimize for li,c, fm,c, np, γp,m . Vp is replaced by using images Ip s.t. pixel p is not in shadow
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Initialization
Set Sp,c to zero , assume constant light intensities and Lambertian scene
1. Obtain initial estimates for surface normals and albedo2. Use these to find initial estimates of the light intensities3. Cluster the albedos to get an initialization of material properties at each
pixel
1) Surface normal and albedo Solve for classical photometric stereo
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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
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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
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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
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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
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Shadow Detection
Maximize
cishadow – single region classifier confidence * region area
cijdiff , cij
diff – pairwise classifier confidence * f(region areas) y – shadow labels for regions
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Shadow Detection
Single region classifier (with χ2 kernel) features1. Color histograms2. Texton histograms
Internal appearance of a given region is not enough Comparison between regions of same material needed
Pairwise region classifier (RBF kernel) features1. Χ2 distance between color and texton histograms2. Ratios of RGB average intensity ( ρr = Ravg1 /Ravg2, …)3. Chromatic alignment (ρr/ρg)4. Normalized distances between the regions
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Pairwise region graph
Different illumination black-white
Same-illumination Green
Not related Orange
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Pairwise region graph
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Apply Graph cut
Reformulate the cost function to apply Graph cut
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Shadow Removal
Soft shadowSolution:Shadow matting
Hard shadow maskSoft shadow matt
Slide from Guo et al.
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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 amount1. Find the fractional shadow coefficients using matting technique2. Find ratio of direct to environment light.Direct
light
Environmental light
Surface Reflectance
Shadow coefficient content from from Guo et al.
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Light Model
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Non-shadow: ti=1Umbra: ti=0 Penumbra = 0 < ti < 1
Relighting : Iishadow-free = (Ldcosθi + Le)Ri
Figure from from Guo et al.
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Shadow model as a matting problem
Ii = γiFi + (1- γi)Bi F: Foreground image, B: background Image
Rewrite shadow model as: Ii = ki(LdRi + LeRi) + (1-ki)LeRi
Similar to matting eqn.
Solve for matting:
minimize E(k) = kTL k + λ(k-k’)TD(k-k’)T
optimal k obtained by solving sparse system: (L + λD)k = λdk’
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Finding the light ratio
Final r obtained by voting
content from from Guo et al.
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Results UCF Dataset (Zhu et al.)
245 images outdoor scenes, manual annotations
content from from Guo et al.
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Experiments: Datasets UCF Dataset (Zhu et al.)
245 images outdoor scenes, manual annotations
content from from Guo et al.
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Experiments: Datasets New UIUC Shadow Dataset
108 images, indoor/outdoor, automatic annotation Evaluate both shadow detection and removal
Input imageGroundtruthNonshadow Shadow mask
content from from Guo et al.
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Results on UCF DatasetInput image
GroundtruthShadow mask Detection Removal result
content from from Guo et al.
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Results on UIUC DatasetInput image Detection Removal
resultGroundtruth
content from from Guo et al.
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Results: Shadow Detection
Accuracy UCF dataset UIUC dataset (ours)
Full model 0.900 0.883Single region 0.875 0.796Zhu et al. 0.887 -
Pixel accuracy
content from from Guo et al.
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Shadow Non-shadow
Shadow (GT) 0.515 0.485
Non-shadow(GT)
0.057 0.947
Results: Shadow Detection
Shadow Non-shadow
Shadow (GT)
0.750 0.250
Non-shadow(GT)
0.070 0.930
Confusion matrices on UCF dataset
full model Single region classification
content from from Guo et al.
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Shadow Non-shadow
Shadow (GT) 0.639 0.361Non-shadow(GT)
0.067 0.934
Results: Shadow Detection
Shadow Non-shadow
Shadow (GT)
0.750 0.250
Non-shadow(GT)
0.070 0.930
Confusion matrices on UCF datasetZhu et al. 2010full model
content from from Guo et al.
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Failure Example
Input image Detection Removal result
content from from Guo et al.
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End