Colour image processing for SHADOW REMOVAL

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 !