ABSTRACT
Camouflaging an object in a photograph is normally performed with the intent of unnoticeably hiding it within a given image. In this work, we give a different dimension to this problem and raise the interesting issue of camouaging motion blur with special relevance to non-uniformly blurred images. Given a blurred photograph, we apply a suitably derived blurring model to smear a target object and naturally blend it into the motion blurred background by using alpha matting. We validate our photo-realistic compositing approach on several synthetic and real examples.
- U. Ananya, S. Muktanidhi, and U. Mudenagudi. Detection of doctored images using correlations of psf. In Proc. Indian Conference on Computer Vision, Graphics and Image Processing, page 56. ACM, 2012. Google ScholarDigital Library
- A. Criminisi, P. Pérez, and K. Toyama. Region filling and object removal by exemplar-based image inpainting. Image Processing, IEEE Transactions on, 13(9):1200–1212, 2004. Google ScholarDigital Library
- S. Darabi, E. Shechtman, C. Barnes, D. B. Goldman, and P. Sen. Image melding: combining inconsistent images using patch-based synthesis. ACM Trans. Graph., 31(4):82, 2012. Google ScholarDigital Library
- H. Farid. Image forgery detection. Signal Processing Magazine, IEEE, 26(2):16–25, 2009.Google ScholarCross Ref
- R. Fergus, B. Singh, A. Hertzmann, S. T. Roweis, and W. T. Freeman. Removing camera shake from a single photograph. ACM Transactions on Graphics, 25(3):787–794, 2006. Google ScholarDigital Library
- A. Gupta, N. Joshi, C. L. Zitnick, M. Cohen, and B. Curless. Single image deblurring using motion density functions. In Computer Vision–ECCV 2010, pages 171–184. Springer, 2010. Google ScholarDigital Library
- D.-Y. Hsiao and S.-C. Pei. Detecting digital tampering by blur estimation. In Systematic Approaches to Digital Forensic Engineering, 2005. First International Workshop on, pages 264–278. IEEE, 2005. Google ScholarDigital Library
- J. Jia. Single image motion deblurring using transparency. IEEE Conference on Computer Vision and Pattern Recognition, 1:1141–1151, 2007.Google ScholarCross Ref
- P. Kakar, N. Sudha, and W. Ser. Exposing digital image forgeries by detecting discrepancies in motion blur. Multimedia, IEEE Transactions on, 13(3):443–452, 2011. Google ScholarDigital Library
- K. Karsch, V. Hedau, D. Forsyth, and D. Hoiem. Rendering synthetic objects into legacy photographs. ACM Transactions on Graphics (TOG), 30(6):157, 2011. Google ScholarDigital Library
- J.-F. Lalonde, D. Hoiem, A. A. Efros, C. Rother, J. Winn, and A. Criminisi. Photo clip art. In ACM Transactions on Graphics (TOG), volume 26, page 3. ACM, 2007. Google ScholarDigital Library
- A. Levin, D. Lischinski, and Y. Weiss. A closed-form solution to natural image matting. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 30(2):228–242, 2008. Google ScholarDigital Library
- J. Liu, S. Ji, and J. Ye. SLEP: Sparse learning with efficient projections, 2009.Google Scholar
- W. Luo, J. Huang, and G. Qiu. Robust detection of region-duplication forgery in digital image. In Pattern Recognition, 2006. ICPR 2006. 18th International Conference on, volume 4, pages 746–749. IEEE, 2006. Google ScholarDigital Library
- T.-T. Ng and S.-F. Chang. A model for image splicing. In Image Processing, 2004. ICIP'04. 2004 International Conference on, volume 2, pages 1169–1172. IEEE, 2004.Google Scholar
- A. Owens, C. Barnes, A. Flint, H. Singh, and W. Freeman. Camouflaging an object from many viewpoints. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2014. Google ScholarDigital Library
- C. Paramanand and A. N. Rajagopalan. Non-uniform motion deblurring for bilayer scenes. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, pages 1113–11202, 2013. Google ScholarDigital Library
- M. P. Rao, A. Rajagopalan, and G. Seetharaman. Harnessing motion blur to unveil splicing. IEEE Transactions on Information Forensics and Security, 9(4):583–595, 2014. Google ScholarDigital Library
- M. Stevens and S. Merilaita. Animal camouflage: current issues and new perspectives. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1516):423–427, 2009.Google ScholarCross Ref
- Y. Tai, N. Kong, S. Lin, and S. Y. Shin. Coded exposure imaging for projective motion deblurring. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2010.Google ScholarCross Ref
- J. Wang and M. F. Cohen. Image and video matting: a survey, volume 3. Now Publishers Inc, 2008. Google ScholarDigital Library
- O. Whyte, J. Sivic, A. Zisserman, and J. Ponce. Non-uniform deblurring for shaken images. International Journal of Computer Vision, 98(2):168–186, 2012. Google ScholarDigital Library
- L. Xu and J. Jia. Two-phase kernel estimation for robust motion deblurring. In Proc. European Conference on Computer Vision, 2010. Google ScholarDigital Library
- S.-K. Yeung, C.-K. Tang, M. S. Brown, and S. B. Kang. Matting and compositing of transparent and refractive objects. ACM Transactions on Graphics (TOG), 30(1):2, 2011. Google ScholarDigital Library
Index Terms
- Camouflaging Motion Blur: Art or Science?
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