![](bga.png)
5 CONCLUSIONS
In this article, some optimizations have been pro-
posed to get a DR system that preserves structures
and works near real-time. Patch search reduction
and downsampling optimizations are valid for a sin-
gle static image, while the global system is mainly
oriented to video applications, where the temporal
coherence let us using tracking techniques to main-
tain the reconstrunction of the image stable along the
video sequence. Two different tracking methods have
been considered to study their influence in the final
image reconstruction and to obtain a robust DR sys-
tem. A battery of experiments has demosntrated a
substantial saving in the computational cost (several
orders of magnitude), while maintaining the visual
perception quality at acceptable levels. The use of
parallel computing techniques is an issue that will be
addressed in the future.
ACKNOWLEDGEMENTS
This work has been partially funded with a Torres
Quevedo grant from the Ministry of Economy and
Competitiveness of the government of Spain.
REFERENCES
Barnes, C., Shechtman, E., Finkelstein, A., and Goldman,
D. (2009). Patchmatch: A randomized correspon-
dence algorithm for structural image editing. ACM
Transactions on Graphics-TOG, 28(3):24.
Bertalmio, M., Sapiro, G., Caselles, V., and Ballester, C.
(2000). Image inpainting. In Proceedings of the 27th
annual conference on Computer graphics and interac-
tive techniques, pages 417–424. ACM Press/Addison-
Wesley Publishing Co.
Bouguet, J.-Y. (2001). Pyramidal implementation of the
affine lucas kanade feature tracker description of the
algorithm. Intel Corporation, 5(1-10):4.
Bradski, G. R. (1998). Real time face and object tracking
as a component of a perceptual user interface. In Ap-
plications of Computer Vision, 1998. WACV’98. Pro-
ceedings., Fourth IEEE Workshop on, pages 214–219.
IEEE.
Criminisi, A., P
´
erez, P., and Toyama, K. (2004). Region
filling and object removal by exemplar-based image
inpainting. Image Processing, IEEE Transactions on,
13(9):1200–1212.
Fischler, M. A. and Bolles, R. C. (1981). Random sample
consensus: A paradigm for model fitting with appli-
cations to image analysis and automated cartography.
Commun. ACM, 24(6):381–395.
Google Inc. (2013). Google AR Glasses. http://
www.google.es/glass/start/.
Goyal, P., Diwakar, S., et al. (2010). Fast and enhanced
algorithm for exemplar based image inpainting. In
Image and Video Technology (PSIVT), 2010 Fourth
Pacific-Rim Symposium on, pages 325–330. IEEE.
Herling, J. and Broll, W. (2010). Advanced self-contained
object removal for realizing real-time diminished real-
ity in unconstrained environments. In Mixed and Aug-
mented Reality (ISMAR), 2010 9th IEEE International
Symposium on, pages 207–212. IEEE.
Herling, J. and Broll, W. (2012). Pixmix: A real-time ap-
proach to high-quality diminished reality. In Mixed
and Augmented Reality (ISMAR), 2012 IEEE Interna-
tional Symposium on, pages 141–150. IEEE.
Itseez (2016). Open source computer vision library.
https://github.com/itseez/opencv.
Kass, M., Witkin, A., and Terzopoulos, D. (1988). Snakes:
Active contour models. International journal of com-
puter vision, 1(4):321–331.
Kawai, N., Sato, T., and Yokoya, N. (2013a). Diminished
reality considering background structures. In Mixed
and Augmented Reality (ISMAR), 2013 IEEE Interna-
tional Symposium on, pages 259–260. IEEE.
Kawai, N., Yamasaki, M., Sato, T., and Yokoya, N. (2013b).
[paper] diminished reality for ar marker hiding based
on image inpainting with reflection of luminance
changes. ITE Transactions on Media Technology and
Applications, 1(4):343–353.
Komodakis, N. and Tziritas, G. (2007). Image completion
using efficient belief propagation via priority schedul-
ing and dynamic pruning. Image Processing, IEEE
Transactions on, 16(11):2649–2661.
Lepetit, V., Berger, M.-O., and Lorraine, L.-I. (2001). An
intuitive tool for outlining objects in video sequences:
Applications to augmented and diminished reality. tC,
2:t3.
Liu, Y. and Caselles, V. (2013). Exemplar-based image in-
painting using multiscale graph cuts. Image Process-
ing, IEEE Transactions on, 22(5):1699–1711.
Lucas, B. D., Kanade, T., et al. (1981). An iterative image
registration technique with an application to stereo vi-
sion. In IJCAI, volume 81, pages 674–679.
Rahul Verma (2013). Inpainting algorithm source. https://
github.com/surahul/Inpaint.
Rosten, E. and Drummond, T. (2005). Fusing points and
lines for high performance tracking. In In Tenth IEEE
International Conference on Computer Vision, pages
1508–1515.
Simakov, D., Caspi, Y., Shechtman, E., and Irani, M.
(2008). Summarizing visual data using bidirectional
similarity. In Computer Vision and Pattern Recogni-
tion, 2008. CVPR 2008. IEEE Conference on, pages
1–8. IEEE.
Wexler, Y., Shechtman, E., and Irani, M. (2007). Space-time
completion of video. Pattern Analysis and Machine
Intelligence, IEEE Transactions on, 29(3):463–476.
Zokai, S., Esteve, J., Genc, Y., and Navab, N. (2003). Multi-
view paraperspective projection model for diminished
reality. In Mixed and Augmented Reality, 2003. Pro-
ceedings. The Second IEEE and ACM International
Symposium on, pages 217–226. IEEE.
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