Abstract
A non-parametric method for texture synthesis is proposed. The texture synthesis process grows a new image outward from an initial seed, one pixel at a time. A Markov random field model is assumed, and the conditional distribution of a pixel given all its neighbors synthesized so far is estimated by querying the sample image and finding all similar neighborhoods. The degree of randomness is controlled by a single perceptually intuitive parameter. The method aims at preserving as much local structure as possible and produces good results for a wide variety of synthetic and real-world textures. 1.
Keywords
Texture synthesisRandomnessParametric statisticsMarkov random fieldPixelImage textureComputer scienceArtificial intelligenceTexture (cosmology)Image (mathematics)Markov processPattern recognition (psychology)Sample (material)Markov chainDegree (music)Sampling (signal processing)Computer visionMathematicsImage processingImage segmentationStatisticsMachine learning
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Publication Info
- Year
- 1999
- Type
- article
- Citations
- 3012
- Access
- Closed
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Cite This
Alexei A. Efros,
Thomas Leung
(1999).
Texture synthesis by non-parametric sampling.
Proceedings of the Seventh IEEE International Conference on Computer Vision
.
https://doi.org/10.1109/iccv.1999.790383
Identifiers
- DOI
- 10.1109/iccv.1999.790383