Texture Synthesis by Non-parametric Sampling

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Texture Synthesis by Non-parametric Sampling (1999)

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by Alexei Efros , Thomas Leung

Venue:	In International Conference on Computer Vision
Citations:	995 - 7 self

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@INPROCEEDINGS{Efros99texturesynthesis,
    author = {Alexei Efros and Thomas Leung},
    title = {Texture Synthesis by Non-parametric Sampling},
    booktitle = {In International Conference on Computer Vision},
    year = {1999},
    pages = {1033--1038}
}

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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. Introduction Texture synthesis has been an active research topic in computer vision both as a way to verify texture analysis methods, as well as in its own right. Potential applications of a successful texture synthesis algorithm are broad, including occlusion fill-in, lossy image and video compression, foreground removal, etc. The problem of texture synthesis can be formulated as follows: let...

Keyphrases

texture synthesis non-parametric sampling video compression successful texture synthesis algorithm real-world texture conditional distribution initial seed introduction texture synthesis lossy image markov random field model sample image intuitive parameter good result computer vision similar neighborhood occlusion fill-in texture analysis method much local structure new image outward active research topic non-parametric method foreground removal texture synthesis process wide variety potential application

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