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

)(

)(

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 !