Image Repairing: Robust Image Synthesis by Adaptive N D Tensor Voting

1. Image Repairing: Robust Image Synthesis by Adaptive ND Tensor Voting IEEE Computer Society Conference on Computer Vision and Pattern Recognition Jiaya Jia, Chi-Keung Tang Computer Science Department The Hong Kong University of Science and Technology

2. Motivation • Main difficulties to repair a severely damaged image of natural scene • Mixture of texture and colors • Inhomogeneity of patterns • Regular object shapes

3. Motivation • Given as few as one image without additional knowledge, we address: • How much color and shape information in the existing part is needed to seamlessly fill the hole? • How good can we achieve in order to reduce possible visual artifact when the information available is not sufficient. • Robust Tensor Voting method is adopted

4. Tensor Voting Review • Tensors: compact representation of information • Tensor encoding: Ball tensor: uncertainty in all directions Stick tensor: certainty along two opposite directions 3D tensor Plate tensor: certainty of directions in a plate

5. Tensor Voting Review • Voting process is to propagate local information Osculating circle P

6. Image repairing system Complete Segmentation Input Damaged Image Texture-based Segmentation Curve Connection Statistical Region Merging Adaptive Scale Selection ND Tensor Voting Output Repaired Image Image synthesis

7. Segmentation • JSEG [Deng and Manjunath 2001] • color quantization • spatial segmentation • Mean shift [Comanicu and Meer 2002] • Deterministic Annealing Framework [Hofmann et al 1998]

8. Texture-based Segmentation

9. Statistical Region Merge • (M + 1)D intensity vector for each region Pi, where M is the maximum color depth in the whole image. if histogram gradient

10. Why Region Merge? • Decrease the complexity of region topology • Relate separate regions P1 P5 P2 Damaged area P3 P4

11. Z P1 P5 P2 P3 P4 X Curve Connection • 2D tensor voting method P2 P4

12. Why Tensor Voting? • The parameter of the voting field can be used to control the smoothness of the resulting curve. • Adaptive to various hole shapes Without hole constraint Small Scale Large Scale With hole constraint

13. Connection Sequence • Topology of surrounding area of the hole can be very complex • Greedy algorithm • Always connect the most similar regions P1 P2 andP4 P5 P3 andP5 P2 P1 Damaged area P3 P4

14. Complete Segmentation

15. Image repairing system Complete Segmentation Input Damaged Image Texture-based Segmentation Curve Connection Statistical Region Merging Adaptive Scale Selection ND Tensor Voting Output Repaired Image Image synthesis

16. ND Tensor Voting • Tensor encoding • Each pixel is encoded as a ND stick tensor 5 5 Scale N=26 Stick tensor

17. ND Tensor Voting • Voting process in ND space • An osculating circle becomes an osculating hypersphere. • ND stick voting field is uniform sampling of normal directions in the ND space. sample sample

18. Adaptive Scaling • texture inhomogeneity in images gives difficulty to assign only one global scale N[Lindeberg et al 1996]. • For each pixel iin images, we calculate: • trace(M) measures the average strength of the square of the gradient magnitude in the window of size Ni

19. Adaptive Scaling • For each sample seed: • Increase its scale Nifrom the lower bound to the upper bound • If trace( ) < trace() - αwhere αis a threshold to avoid small perturbation or noise interference, set Ni - 1 → Niand return • Otherwise, continue the loop until maxima or upper bound is reached

20. Results

21. Results

22. Results

23. Results

24. Results

25. Limitations • Lack of samples. • Meaningful and semi-regular objects.

26. Conclusion • An automatic image repairing system. • Region partition and merging. • Curve connection by 2D tensor voting. • ND tensor voting based image synthesis. • Adaptive scale.