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Colour image processing for 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. Alex’s angels team.

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Colour image processing for SHADOW REMOVAL

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  1. 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

  2. Alex’s angels team Weronika Alex Alina Kasia

  3. Layout • Problem statement • The System Overview • Simulations and Results • Future Perspectives • Conclusions

  4. The System Overview

  5. Histogram 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

  6. Histogram SegmentationResults works well on simple images Original image Otsu Pal

  7. K-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: -> -> ->

  8. K-means Results • automatic computing of number of classes/clusters -> peak’s histogram detection Original image Output image

  9. Expectation 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

  10. Expectation Maximization Results • automatic computing of number of classes/clusters -> peak’s histogram detection

  11. Illuminant invariant images • RGB -> 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

  12. Shadow Removal • Illumination 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

  13. 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.

  14. Illuminant invariant images & Shadow removal Results

  15. Future Perspectives

  16. Future Perspectives

  17. Future Perspectives • To be in contact with all participants of SSIP

  18. Conclusions • The 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

  19. Thank you for your attention !

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