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Issue No.02 - March-April (1997 vol.17)
pp: 18-29
Rendering is one of the most important tasks in computer graphics and animation. It is widely recognized that texture maps are essential for adding to the visual content of the rendered image. Extraction of textures from a single photograph poses severe difficulties and is sometimes impossible, while artificial texture synthesis does not address the full range of desired textures. In this paper we present a method for computing high-quality, multiresolution textures from an image sequence. The method has the following features: (1) it can be used with images in which the textures are present in different resolutions and different perspective distortions; (2) it can extract textures from objects with any known 3D geometric structure; specifically, we are not restricted to planar textures; (3) removal of directional illumination artifacts such as highlights and reflections; (4) efficient storage of the resulting texture in a multiresolution data structure; and (5) no restrictions are imposed on the computed texture, which can be a constant color texture or a richly colored one. We present an especially attractive application of our technique, in which an existing real object participates in an animation sequence and is endowed with synthetic behavior.
texture, multiresolution, highlight, rendering, augmented reality.
Erez Shilat, Eyal Ofek, Michael Werman, "Multiresolution Textures from Image Sequences", IEEE Computer Graphics and Applications, vol.17, no. 2, pp. 18-29, March-April 1997, doi:10.1109/38.574667
1. J. Kajiya, “The Rendering Equation,” Computer Graphics, pp. 143-150, 1986.
2. P.R. Burt and R.J. Kolczynski, “Enhanced Image Capture through Fusion,” Proc. Int'l Conf. Computer Vision, pp. 173-182, May 1993.
3. M. Irani and S. Peleg, "Super Resolution from Image Sequences," Proc. 10th Int'l. Conf. on Pattern Recognition, IEEE Computer Society Press, Los Alamitos, Calif., 1990, pp. 115-120.
4. D. Berman, J. Bartell, and D. Salesin, “Multiresolution Painting and Compositing,” Siggraph 94 Conf. Proc., ACM, New York, pp. 85-90.
5. L.B. Wolff, "Scene Understanding from Propagation and Consistency of Polarization-based Constraints," Proc. Computer Vision and Pattern Recognition, IEEE Press, Piscataway, N.J., 1994, pp. 1000-1004.
6. G.E. Healey, S.A. Shafer, and L.B. Wolff, Physics-Based Vision Principles and Practice, Color. Boston: Jones and Bartlett, 1992.
7. S.A. Shafer, "Using Color to Separate Reflection Components," Color Research and Application, Vol. 10, No. 4, Winter 1985, pp. 43-51.
8. S.W. Lee and R. Bajcsy, "Detection of Specularity Using Color and Multiple Views," Image and Vision Computing, Vol. 10, No. 10, Dec. 1992, pp. 643-653.
9. E. Shilat et al., "Tracking a Rigid Object Along Image Sequences Using a Three-Frame Matching Primitive," Tech. Report, Institute of Computer Science, Hebrew University of Jerusalem,
10. M. Oren and S.K. Nayar, “Generalization of the Lambertian Model and Implications for Machine Vision,” Int'l J. Computer Vision, vol. 14, no. 3, pp. 227-251, 1995.
11. L.B. Wolf, "Diffuse and Specular Reflection from Dielectric Surfaces," Image Understanding Workshop, Morgan Kaufman, San Mateo, Calif., 1993, pp. 1025-1030.
12. A. Blake and H. Bülthoff, "Shape from Specularities: Computation and Psychophysics," Phil. Trans. Royal Soc. of London, B 331, 1991, pp. 237-252.
13. L. Williams, "Pyramidal Parametrics, Computer Graphics," vol. 17, no. 3, pp. 1-11, July 1983.
14. Y. Zakai and A. Rappoport, "Three-Dimensional Modeling and Effects on Still Images, Computer Graphics Forum (Proc. Eurographics), Vol. 15, No. 3, 1996, pp. 3-10.
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