The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Texture synthesis via a noncausal nonparametric multiscale Markov random field
Abstract
Our noncausal, nonparametric, multiscale, Markov random field (MRF) model is capable of synthesizing and capturing the characteristics of a wide variety of textures, from the highly structured to the stochastic. We use a multiscale synthesis algorithm incorporating local annealing to obtain larger realizations of texture visually indistinguishable from the training texture.
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Citations
Cites
Textons, the elements of texture perception, and their interactions
Julesz, B. (1981) · Nature · doi:10.1038/290091a0 · Cited by 1,703 · cited 1×
“Current texture models such as fractal models, auto-models, autoregressive models, moving average models, autoregressive moving average models, do not realistically reproduce natural textures [9], such as those in the Brodatz album [4].”
Background
Cited by
Roth, S., & Black, M. J. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-008-0197-6 · Cited by 847
Texture optimization for example-based synthesis
Kwatra, V., Essa, I., Bobick, A., & Kwatra, N. (2005) · ACM Transactions on Graphics · doi:10.1145/1073204.1073263 · Cited by 608
Transformation of General Binary MRF Minimization to the First-Order Case
Ishikawa, H. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.91 · Cited by 144
Metadata
- Authors
- Paget, R., & Longstaff, I. D.
- Venue
- IEEE Transactions on Image Processing
- Published
- 1998-06
- Volume
- 7
- Issue
- 6
- Pages
- 925-931
- ISSN
- 1057-7149 (ISSN-L)
- OpenAlex
- W2099835437
- Cited by
- 181
- References
- 1
- FWCI
- 3.61
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Texture synthesis via a noncausal nonparametric multiscale Markov random field. https://app.cochat.ai/papers/10.1109/83.679446
- The paper
- Paget, R., & Longstaff, I. D. (1998). Texture synthesis via a noncausal nonparametric multiscale Markov random field. IEEE Transactions on Image Processing. https://doi.org/10.1109/83.679446
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
