outdoor image processing

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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.

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.

Experiments: Datasets UCF Dataset (Zhu et al.)

245 images outdoor scenes, manual annotations

content from from Guo et al.

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.

Results on UCF DatasetInput image

GroundtruthShadow mask Detection Removal result

content from from Guo et al.

Results on UIUC DatasetInput image Detection Removal

resultGroundtruth

content from from Guo et al.

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.

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.

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.

Failure Example

Input image Detection Removal result

content from from Guo et al.

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End