Texture (cosmology)

Pixel

Statistics

Markov process

Chromatography

Markov chain

Degree (music)

Computer science

Acoustics

Filter (signal processing)

Markov random field

Texture synthesis

Image segmentation

Physics

Randomness

Pattern recognition (psychology)

Mathematics

Image (mathematics)

Sampling (signal processing)

Artificial intelligence

Parametric statistics

Chemistry

Sample (material)

Machine learning

Image texture

Computer vision

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.

Texture synthesis by non-parametric sampling