Abstract
We present an algorithm for synthesizing textures from an input sample. This patch-based sampling algorithm is fast and it makes high-quality texture synthesis a real-time process. For generating textures of the same size and comparable quality, patch-based sampling is orders of magnitude faster than existing algorithms. The patch-based sampling algorithm works well for a wide variety of textures ranging from regular to stochastic. By sampling patches according to a nonparametric estimation of the local conditional MRF density function, we avoid mismatching features across patch boundaries. We also experimented with documented cases for which pixel-based nonparametric sampling algorithms cease to be effective but our algorithm continues to work well.
- ARYA, S., MOUNT, D. M., NETANYAHU,N.S.,SILVERMAN, R., AND WU, A. Y. 1998. An optimal algorithm for approximate nearest neighbor searching. J. ACM 45, 891-923. Google Scholar
- ASHIKHMIN, M. 2001. Synthesizing natural textures. In Proceedings of the ACM Symposium on Interactive 3D Graphics (March), 217-226. Google Scholar
- BAR-JOSEPH, Z., EL-YANIV, R., LISCHINSKI,D.,AND WERMAN, M. 2001. Texture mixing and texture movie synthesis using statistical learning. IEEE Trans. Vis. Comput. Graph. Google Scholar
- DE BONET, J. S. 1997. Multiresolution sampling procedure for analysis and synthesis of texture image. In Computer Graphics Proceedings, Annual Conference Series (August), 361-368. Google Scholar
- EFROS,A.AND FREEMAN, W. 2001. Image quilting for texture synthesis and transfer. In Computer Graphics Proceedings, Annual Conference Series (August). Google Scholar
- EFROS,A.A.AND LEUNG, T. K. 1999. Texture synthesis by non-parametric sampling. In Proceedings of International Conference on Computer Vision. Google Scholar
- FOURNIER, A., FUSSELL,D.,AND CARPENTER, L. 1982. Computer rendering of stochastic models. Commun. ACM 25, 6 (June), 371-384. Google Scholar
- FRIEDMAN, J., BENTLEY,J.,AND FINKEL, R. 1977. An algorithm for finding best matches in logarithmic expected time. ACM Trans. Math. Softw. 3, 3, 209-226. Google Scholar
- HEEGER,D.J.AND BERGEN, J. R. 1995. Pyramid-based texture analysis/synthesis. In Computer Graphics Proceedings, Annual Conference Series (July), 229-238. Google Scholar
- HERTZMANN, A., JACOBS, C., OLIVER, N., CURLESS,B.,AND SALESIN, D. 2001. Image analogies. In Computer Graphics Proceedings, Annual Conference Series (August). Google Scholar
- IVERSEN,H.AND LONNESTAD, T. 1994. An evaluation of stochastic models for analysis and synthesis of gray scale texture. Pattern Recogn. Lett. 15, 575-585. Google Scholar
- JOLLIFE, I. T. 1986. Principal Component Analysis. Springer-Verlag, New York.Google Scholar
- LEWIS, J.-P. 1984. Texture synthesis for digital painting. In Comput. Graph. (SIGGRAPH '84 Proceedings) 18, 245-252. Google Scholar
- LIANG, L., LIU, C., XU,Y.Q.,GUO,B.,AND SHUM, H. Y. 2001. Real-time texture synthesis by patchbased sampling. Microsoft Research Tech. Rep. MSR-TR-2001-40, March.Google Scholar
- MOUNT, D. M. 1998. ANN Programming Manual. Department of Computer Science, University of Maryland, College Park, Maryland.Google Scholar
- NENE,S.A.AND NAYAR, S. K. 1997. A simple algorithm for nearest-neighbor search in high dimensions. IEEE Trans. PAMI 19, 9 (Sept.), 989-1003. Google Scholar
- PERLIN, K. 1985. An image synthesizer. Comput. Graph. (Proceedings of SIGGRAPH '85) 19,3 (July), 287-296. Google Scholar
- POPAT,K.AND PICARD, R. W. 1993. Novel cluster-based probability model for texture synthesis, classification, and compression. In Proceedings of SPIE Visual Communication and Image Processing, 756-768.Google Scholar
- PORTILLA,J.AND SIMONCELLI, E. 1999. Texture modeling and synthesis using joint statistics of complex wavelet coefficients. In Proceedings of the IEEE Workshop on Statistical and Computational Theories of Vision.Google Scholar
- PRAUN, E., FINKELSTEIN, A., AND HOPPE, H. 2000. Lapped texture. In Computer Graphics Proceedings, Annual Conference Series (July), 465-470. Google Scholar
- SZELISKI,R.AND SHUM, H.-Y. 1997. Creating full view panoramic mosaics and environment maps. In Proceedings of SIGGRAPH '97 (August), 251-258. Google Scholar
- TURK, G. 1991. Generating textures on arbitrary surfaces using reaction-diffusion. In Computer Graphics (SIGGRAPH '91 Proceedings) 25 (July), 289-298. Google Scholar
- TURK, G. 2001. Texture synthesis on surfaces. In Computer Graphics Proceedings, Annual Conference Series (August). Google Scholar
- WEI,L.AND LEVOY, M. 2001. Texture synthesis over arbitrary manifold surfaces. In Computer Graphics Proceedings, Annual Conference Series (August). Google Scholar
- WEI,L.Y.AND LEVOY, M. 2000. Fast texture synthesis using tree-structured vector quantization. In Computer Graphics Proceedings, Annual Conference Series (July), 479-488. Google Scholar
- WITKIN,A.AND KASS, M. 1991. Reaction-diffusion textures. In Computer Graphics (SIGGRAPH '91 Proceedings), 25, (July), 299-308. Google Scholar
- WORLEY, S. P. 1996. A cellular texturing basis function. In SIGGRAPH'96 Conference Proceedings, Holly Rushmeier, Ed., Annual Conference Series (August), 291-294. Google Scholar
- WU,Y.N.,ZHU,S.C.,AND LIU, X. W. 2000. Equivalence of Julesz ensemble and FRAME models. Int. J. Comput. Vis. 38, 30, 245-261. Google Scholar
- XU, Y. Q., GUO,B.,AND SHUM, H. Y. 2000. Chaos mosaic: Fast and memory efficient texture synthesis. Microsoft Res. Tech. Rep. MSR-TR-2000-32, April.Google Scholar
- YING, L., HERTZMANN, A., BIERMANN, H., AND ZORIN, D. 2001. Texture and shape synthesis on surfaces. In Proceedings of the Twelfth Eurographics Workshop on Rendering (June). Google Scholar
- ZHU,S.C.,LIU, X., AND WU, Y. 2000. Exploring texture ensembles by efficient Markov chain Monte Carlo. IEEE Trans. PAMI 22,6. Google Scholar
- ZHU,S.C.,WU,Y.,AND MUMFORD, D. B. 1997. Minimax entropy principle and its application to texture modeling. Neural Comput. 9, 1627-1660 (first appeared in CVPR '96). Google Scholar
- ZHU,S.C.,WU,Y.,AND MUMFORD, D. 1998. Filters, random-fields and maximum-entropy (Frame). Int. J. Comput. Vis. 27, 2 (March), 107-126. Google Scholar
- ZUCKER,S.AND TERZOPOULOS, D. 1980. Finding structure in co-occurence matrices for texture analysis. Comput. Graph. Image Process. 12, 286-307.Google Scholar
Index Terms
- Real-time texture synthesis by patch-based sampling
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