“Deviation magnification: revealing departures from ideal geometries” by Wadhwa, Dekel, Wei, Durand and Freeman
Conference:
Type(s):
Title:
- Deviation magnification: revealing departures from ideal geometries
Session/Category Title: Single Images
Presenter(s)/Author(s):
Abstract:
Structures and objects are often supposed to have idealized geometries such as straight lines or circles. Although not always visible to the naked eye, in reality, these objects deviate from their idealized models. Our goal is to reveal and visualize such subtle geometric deviations, which can contain useful, surprising information about our world. Our framework, termed Deviation Magnification, takes a still image as input, fits parametric models to objects of interest, computes the geometric deviations, and renders an output image in which the departures from ideal geometries are exaggerated. We demonstrate the correctness and usefulness of our method through quantitative evaluation on a synthetic dataset and by application to challenging natural images.
References:
1. Blanz, V., and Vetter, T. 1999. A morphable model for the synthesis of 3d faces. In Proceedings of the 26th annual conference on Computer graphics and interactive techniques, ACM Press/Addison-Wesley Publishing Co., 187–194.
2. Canny, J. 1986. A computational approach to edge detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 6, 679–698.
3. Dekel, T., Michaeli, T., Irani, M., and Freeman, W. T. 2015. Revealing and modifying non local variations in a single image. ACM Transactions on Graphics (TOG) special issue. Proc. SIGGRAPH Asia.
4. Dollár, P., and Zitnick, C. L. 2013. Structured forests for fast edge detection. In Computer Vision (ICCV), 2013 IEEE International Conference on, IEEE, 1841–1848.
5. Dollar, P., Tu, Z., and Belongie, S. 2006. Supervised learning of edges and object boundaries. In Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, vol. 2, IEEE, 1964–1971.
6. Duda, R. O., and Hart, P. E. 1972. Use of the hough transformation to detect lines and curves in pictures. Communications of the ACM 15, 1, 11–15.
7. DxO OpticsPro 10. http://www.dxo.com/us/photography/photo-software/dxo-opticspro.
8. Elgharib, M., Hefeeda, M., Durand, F., and Freeman, W. T. 2015. Video magnification in presence of large motions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4119–4127.
9. Fischler, M. A., and Bolles, R. C. 1981. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24, 6, 381–395.
10. Freeman, W. T., and Adelson, E. H. 1991. The design and use of steerable filters. IEEE Transactions on Pattern analysis and machine intelligence 13, 9, 891–906.
11. Grossman, W. M. 2012. Time shift: Is london’s big ben falling down? Scientific American.
12. Hargather, M. J., and Settles, G. S. 2010. Natural-background-oriented schlieren imaging. Experiments in fluids 48, 1, 59–68.
13. Hartley, R., and Zisserman, A. 2003. Multiple view geometry in computer vision. Cambridge university press.
14. Levin, A., Lischinski, D., and Weiss, Y. 2004. Colorization using optimization. 689–694.
15. Levin, A., Lischinski, D., and Weiss, Y. 2008. A closed-form solution to natural image matting. Pattern Analysis and Machine Intelligence, IEEE Transactions on 30, 2, 228–242.
16. Lim, J. J., Zitnick, C. L., and Dollár, P. 2013. Sketch tokens: A learned mid-level representation for contour and object detection. In Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, IEEE, 3158–3165.
17. Liu, C., Torralba, A., Freeman, W. T., Durand, F., and Adelson, E. H. 2005. Motion magnification. In ACM Transactions on Graphics (TOG), vol. 24, ACM, 519–526.
18. Lucas, B. D., Kanade, T., et al. 1981. An iterative image registration technique with an application to stereo vision. In IJCAI, vol. 81, 674–679.
19. Nalwa, V. S., and Binford, T. O. 1986. On detecting edges. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 6, 699–714.
20. Nichols, G. 2009. Sedimentology and stratigraphy. John Wiley & Sons.
21. Pătrăucean, V., Gurdjos, P., and Von Gioi, R. G. 2012. A parameterless line segment and elliptical arc detector with enhanced ellipse fitting. In Computer Vision–ECCV 2012. Springer, 572–585.
22. Sutton, P. J., and Kusmartsev, F. V. 2013. Gravitational vortices and clump formation in saturn’s f ring during an encounter with prometheus. Scientific reports 3.
23. Tritton, D. J. 1988. Physical fluid dynamics. Oxford, Clarendon Press, 1988, 536 p. 1.
24. Wadhwa, N., Rubinstein, M., Durand, F., and Freeman, W. T. 2013. Phase-based video motion processing. ACM Transactions on Graphics (TOG) 32, 4, 80.
25. Wadhwa, N., Rubinstein, M., Durand, F., and Freeman, W. T. 2014. Riesz pyramid for fast phase-based video magnification. In Computational Photography (ICCP), 2014 IEEE International Conference on. IEEE.
26. Wu, H.-Y., Rubinstein, M., Shih, E., Guttag, J. V., Durand, F., and Freeman, W. T. 2012. Eulerian video magnification for revealing subtle changes in the world. ACM Trans. Graph. 31, 4, 65.
27. Xue, T., Rubinstein, M., Wadhwa, N., Levin, A., Durand, F., and Freeman, W. T. 2014. Refraction wiggles for measuring fluid depth and velocity from video. In Computer Vision–ECCV 2014. Springer, 767–782.
