Transcript SHADOW REMOVAL

Colour image processing for
SHADOW REMOVAL
Alina Elena Oprea, University Politehnica of Bucharest
Katarzyna Balakier, Fundacion SENER
Weronika Piatkowska, Jagiellonian University
Alexandru Popa, Technical University of Cluj-Napoca
Summer School on Image Processing 2009, Debrecen, Hungary
Alex’s angels team
Weronika
Alex
Alina
Kasia
Summer School on Image Processing 2009, Debrecen, Hungary
Layout
Problem statement
 The System Overview
 Simulations and Results
 Future Perspectives
 Conclusions

Summer School on Image Processing 2009, Debrecen, Hungary
The System Overview
•
•
SHADOW •
DETECTION •
SHADOW
REMOVAL
Histogram segmentation approach
K-means approach
Expectation Maximization
Illuminant invariant images
• Illumination recovery + Inpainting
• Second method
Summer School on Image Processing 2009, Debrecen, Hungary
Histogram Segmentation
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Automatically Picking a Threshold:
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Otsu thresholding method:
minimization of the weighted within-class variance / maximization of
the inter-class variance;
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Pal thresholding method:
concept of cross-entropy maximization
Summer School on Image Processing 2009, Debrecen, Hungary
Histogram Segmentation
Results
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works well on simple images
Original image
Otsu
Pal
K-means
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k-means clustering = method of cluster analysis -> partitions n
observations into k clusters in which each observation belongs to the
cluster with the nearest mean;
set of observations (x1, x2, …, xn) -> partition the n observations
into k sets (k < n)
Basic steps:
->
->
->
Summer School on Image Processing 2009, Debrecen, Hungary
K-means Results

automatic computing of number of classes/clusters -> peak’s
histogram detection
Original image
Output image
Summer School on Image Processing 2009, Debrecen, Hungary
Expectation Maximization
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EM algorithm :maintains probabilistic assignments to clusters, instead of
deterministic assignments;
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E step: assign points to the model that fits it best
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M step: update the parameters of the models using only points assigned
to it
Summer School on Image Processing 2009, Debrecen, Hungary
Expectation Maximization Results

automatic computing of number of classes/clusters -> peak’s
histogram detection
Summer School on Image Processing 2009, Debrecen, Hungary
Illuminant invariant images
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RGB -> 2D log-chromaticity co-ordinates:
◦ r = log(R) – log(G)
◦ b = log(B) – log(G)
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the r and b co-ordinates varies when illumination changes;
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the pair (r,b) for a single surface viewed under many different lights - a
line in the chromaticity space;
projecting orthogonally to this line results in a 1D value which is
invariant to illumination;
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by subtracting from the grayscale image the illuminant invariant, we
obtain a perfect mask of the shadow
Summer School on Image Processing 2009, Debrecen, Hungary
Shadow Removal
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Illumination recovery
◦ recover the illuminated intensity at a shadowed pixel -estimate the
four parameters of the affine model:
I k ( p )  k ( p )   ( p ) I k
lit
shadow
( p)
◦ two strips of pixels: one inside the shadowed region, and the other
outside the region
S -> shadowed set of pixels
◦ L -> illuminated set of pixels
◦  ( S ) and
 (L )denote the mean colors of pixels from S and L
)
◦  ( S ) and  ( L denote
the standard deviations
 
 (L)
 (S )
 k   k ( L )   k ( S )
Summer School on Image Processing 2009, Debrecen, Hungary
Shadow Removal

Inpainting
◦ the patch lies on the continuation of an image edge, the most likely
best matches will lie along the same (or a similarly colored) edge
◦ the algorithm is divided in 3 steps:
 compute patch priorities;
 propagate texture and structure information;
 update confidence values.
Summer School on Image Processing 2009, Debrecen, Hungary
Illuminant invariant images &
Shadow removal Results
Summer School on Image Processing 2009, Debrecen, Hungary
Future Perspectives
Summer School on Image Processing 2009, Debrecen, Hungary
Future Perspectives
Summer School on Image Processing 2009, Debrecen, Hungary
Future Perspectives

To be in contact with all participants of
SSIP
Summer School on Image Processing 2009, Debrecen, Hungary
Conclusions
The proposed method is fully automatic (no
user interaction)
 Several methods of shadow detecting have
been applied and good reasults have been
reached
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The methods of shadow removal should be
improved for complex images
Summer School on Image Processing 2009, Debrecen, Hungary
Thank you for your attention !
Summer School on Image Processing 2009, Debrecen, Hungary