Download - Colour image processing for SHADOW REMOVAL
Summer School on Image Processing 2009, Debrecen, Hungary
Colour image processing Colour image processing for for SHADOW REMOVALSHADOW 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 angelsAlex’s angels team team
Weronika Alex Alina Kasia
Summer School on Image Processing 2009, Debrecen, Hungary
LayoutLayout
Problem statementThe System OverviewSimulations and ResultsFuture PerspectivesConclusions
Summer School on Image Processing 2009, Debrecen, Hungary
The System OverviewThe System Overview
Summer School on Image Processing 2009, Debrecen, Hungary
Histogram SegmentationHistogram Segmentation
Automatically Picking a Threshold:
Otsu thresholding method:
- minimization of the weighted within-class variance / maximization of the inter-class variance;
Pal thresholding method:
- concept of cross-entropy maximization
Histogram SegmentationHistogram SegmentationResultsResults
works well on simple images
Original image Otsu Pal
Summer School on Image Processing 2009, Debrecen, Hungary
KK-means-means 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 ResultsK-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 MaximizationExpectation Maximization EM algorithm :maintains probabilistic assignments to
clusters, instead of deterministic assignments;
E step: assign points to the model that fits it best
M step: update the parameters of the models using only points assigned to it
Summer School on Image Processing 2009, Debrecen, Hungary
Expectation Maximization Expectation Maximization ResultsResults automatic computing of number of classes/clusters ->
peak’s histogram detection
Summer School on Image Processing 2009, Debrecen, Hungary
Illuminant invariant imagesIlluminant invariant imagesRGB -> 2D log-chromaticity co-ordinates:
◦ r = log(R) – log(G)◦ b = log(B) – log(G)
the r and b co-ordinates varies when illumination changes;
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;
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 RemovalShadow RemovalIllumination recovery
◦ recover the illuminated intensity at a shadowed pixel -estimate the four parameters of the affine model:
◦ 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
◦ and denote the mean colors of pixels from S and L
◦ and denote the standard deviations
)()()()( pIpppI shadowkk
litk
)(S )(L)(S )(L
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S
L
)()( SL kkk
Summer School on Image Processing 2009, Debrecen, Hungary
Shadow RemovalShadow 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 imagesIlluminant invariant images & & Shadow removalShadow removal Results Results
Summer School on Image Processing 2009, Debrecen, Hungary
Future Perspectives Future Perspectives
Summer School on Image Processing 2009, Debrecen, Hungary
Future Perspectives Future Perspectives
Summer School on Image Processing 2009, Debrecen, Hungary
Future Perspectives Future Perspectives
To be in contact with all participants of SSIP
Summer School on Image Processing 2009, Debrecen, Hungary
ConclusionsConclusionsThe proposed method is fully
automatic (no user interaction)Several methods of shadow
detecting have been applied and good reasults have been reached
The methods of shadow removal should be improved for complex images
Summer School on Image Processing 2009, Debrecen, Hungary
Thank you for your Thank you for your attention !attention !