The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Texture optimization for example-based synthesis
Abstract
We present a novel technique for texture synthesis using optimization. We define a Markov Random Field (MRF)-based similarity metric for measuring the quality of synthesized texture with respect to a given input sample. This allows us to formulate the synthesis problem as minimization of an energy function, which is optimized using an Expectation Maximization (EM)-like algorithm. In contrast to most example-based techniques that do region-growing, ours is a joint optimization approach that progressively refines the entire texture. Additionally, our approach is ideally suited to allow for controllable synthesis of textures. Specifically, we demonstrate controllability by animating image textures using flow fields. We allow for general two-dimensional flow fields that may dynamically change over time. Applications of this technique include dynamic texturing of fluid animations and texture-based flow visualization.
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Cites
The EM Algorithm and Extensions
Kushary, D. (1998) · Technometrics · doi:10.1080/00401706.1998.10485534 · Cited by 5,417
Hierarchical Clustering Schemes
Johnson, S. C. (1967) · Psychometrika · doi:10.1007/bf02289588 · Cited by 4,897
Texture synthesis by non-parametric sampling
Efros, A. A., & Leung, T. K. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790383 · Cited by 3,023
2011 International Conference on Computer Vision
(2011) · doi:10.1109/iccv17851.2011 · Cited by 2,860
Example-based super-resolution
Freeman, W. T., Jones, T. R., & Pasztor, E. C. (2002) · IEEE Computer Graphics and Applications · doi:10.1109/38.988747 · Cited by 2,498
Image quilting for texture synthesis and transfer
Efros, A. A., & Freeman, W. T. (2001) · Proceedings of the 28th annual conference on Computer graphics and interactive techniques · doi:10.1145/383259.383296 · Cited by 2,347
Pérez, P., Gangnet, M., & Blake, A. (2003) · ACM SIGGRAPH 2003 Papers · doi:10.1145/1201775.882269 · Cited by 2,020
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
Portilla, J., & Simoncelli, E. P. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026553619983 · Cited by 1,806
Pérez, P., Gangnet, M., & Blake, A. (2003) · ACM Transactions on Graphics · doi:10.1145/882262.882269 · Cited by 1,773
Hertzmann, A., Jacobs, C. E., Oliver, N., Curless, B., & Salesin, D. H. (2001) · Proceedings of the 28th annual conference on Computer graphics and interactive techniques · doi:10.1145/383259.383295 · Cited by 1,518
Fast texture synthesis using tree-structured vector quantization
Wei, L.-Y., & Levoy, M. (2000) · Proceedings of the 27th annual conference on Computer graphics and interactive techniques - SIGGRAPH '00 · doi:10.1145/344779.345009 · Cited by 1,356
Graphcut textures: image and video synthesis using graph cuts
Kwatra, V., Schödl, A., Essa, I., Turk, G., & Bobick, A. (2003) · ACM SIGGRAPH 2003 Papers · doi:10.1145/1201775.882264 · Cited by 1,216
Self-citationPyramid-based texture analysis/synthesis
Heeger, D. J., & Bergen, J. R. (1995) · Proceedings of the 22nd annual conference on Computer graphics and interactive techniques - SIGGRAPH '95 · doi:10.1145/218380.218446 · Cited by 1,095
Graphcut textures: image and video synthesis using graph cuts
Kwatra, V., Schödl, A., Essa, I., Turk, G., & Bobick, A. (2003) · ACM Transactions on Graphics · doi:10.1145/882262.882264 · Cited by 787
Self-citationAshikhmin, M. (2001) · Proceedings of the 2001 symposium on Interactive 3D graphics · doi:10.1145/364338.364405 · Cited by 737
Using the triangle inequality to accelerate k-means
Elkan, C. (2003) · Cited by 705
Wiley Series in Probability and Statistics
(2007) · Wiley Series in Probability and Statistics · doi:10.1002/9780470065662.scard · Cited by 693
Video Rewrite: driving visual speech with audio
Bregler, C., Covell, M., & Slaney, M. (1997) · Proceedings of the 24th annual conference on Computer graphics and interactive techniques - SIGGRAPH '97 · doi:10.1145/258734.258880 · Cited by 669
Real-time texture synthesis by patch-based sampling
Liang, L., Liu, C., Xu, Y.-Q., Guo, B., & Shum, H.-Y. (2001) · ACM Transactions on Graphics · doi:10.1145/501786.501787 · Cited by 655
Schödl, A., Szeliski, R., Salesin, D. H., & Essa, I. (2000) · Proceedings of the 27th annual conference on Computer graphics and interactive techniques - SIGGRAPH '00 · doi:10.1145/344779.345012 · Cited by 571
Self-citationMultiresolution sampling procedure for analysis and synthesis of texture images
De Bonet, J. S. (1997) · Proceedings of the 24th annual conference on Computer graphics and interactive techniques - SIGGRAPH '97 · doi:10.1145/258734.258882 · Cited by 570
Wexler, Y., Shechtman, E., & Irani, M. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315022 · Cited by 347
Trainable videorealistic speech animation
Ezzat, T., Geiger, G., & Poggio, T. (2002) · Proceedings of the 29th annual conference on Computer graphics and interactive techniques · doi:10.1145/566570.566594 · Cited by 301
Epitomic analysis of appearance and shape
Jojic, Frey, & Kannan (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238311 · Cited by 218
Vector field design on surfaces
Zhang, E., Mischaikow, K., & Turk, G. (2006) · ACM Transactions on Graphics · doi:10.1145/1183287.1183290 · Cited by 215
Texture synthesis via a noncausal nonparametric multiscale Markov random field
Paget, R., & Longstaff, I. D. (1998) · IEEE Transactions on Image Processing · doi:10.1109/83.679446 · Cited by 181
Feature matching and deformation for texture synthesis
Wu, Q., & Yu, Y. (2004) · ACM Transactions on Graphics · doi:10.1145/1015706.1015730 · Cited by 180
Image-based rendering using image-based priors
Fitzgibbon, Wexler, & Zisserman (2003) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2003.1238625 · Cited by 157
A System of Subroutines for Iteratively Reweighted Least Squares Computations
Coleman, D., Holland, P. W., Kaden, N., Klema, V. C., & Peters, S. C. (1980) · ACM Transactions on Mathematical Software · doi:10.1145/355900.355905 · Cited by 101
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Metadata
- Authors
- Kwatra, V., Essa, I., Bobick, A., & Kwatra, N.
- Venue
- ACM Transactions on Graphics
- Published
- 2005-07
- Volume
- 24
- Issue
- 3
- Pages
- 795-802
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W1967577110
- Cited by
- 608
- References
- 29
- FWCI
- 52.04
- Type
- journal article
- Language
- en
- Open access
- closed
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