• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

DMCA

Fast texture synthesis using tree-structured vector quantization (2000)

Cached

  • Download as a PDF

Download Links

  • [www.cs.stevens.edu]
  • [graphics.stanford.edu]
  • [www-graphics.stanford.edu]
  • [graphics.stanford.edu]
  • [www.cs.brown.edu]
  • [www-hci.stanford.edu]
  • [www-graphics.stanford.edu]
  • [graphics.stanford.edu]
  • [cgm.cs.ntust.edu.tw]
  • [artis.imag.fr]
  • [graphics.stanford.edu]
  • [www-graphics.stanford.edu]
  • [www.visgraf.impa.br]

  • Other Repositories/Bibliography

  • DBLP
  • Save to List
  • Add to Collection
  • Correct Errors
  • Monitor Changes
by Li-yi Wei , Marc Levoy
Citations:561 - 12 self
  • Summary
  • Citations
  • Active Bibliography
  • Co-citation
  • Clustered Documents
  • Version History

BibTeX

@INPROCEEDINGS{Wei00fasttexture,
    author = {Li-yi Wei and Marc Levoy},
    title = {Fast texture synthesis using tree-structured vector quantization},
    booktitle = {},
    year = {2000},
    pages = {479--488}
}

Share

Facebook Twitter Reddit Bibsonomy

OpenURL

 

Abstract

Figure 1: Our texture generation process takes an example texture patch (left) and a random noise (middle) as input, and modifies this random noise to make it look like the given example texture. The synthesized texture (right) can be of arbitrary size, and is perceived as very similar to the given example. Using our algorithm, textures can be generated within seconds, and the synthesized results are always tileable. Texture synthesis is important for many applications in computer graphics, vision, and image processing. However, it remains difficult to design an algorithm that is both efficient and capable of generating high quality results. In this paper, we present an efficient algorithm for realistic texture synthesis. The algorithm is easy to use and requires only a sample texture as input. It generates textures with perceived quality equal to or better than those produced by previous techniques, but runs two orders of magnitude faster. This permits us to apply texture synthesis to problems where it has traditionally been considered impractical. In particular, we have applied it to constrained synthesis for image editing and temporal texture generation. Our algorithm is derived from Markov Random Field texture models and generates textures through a deterministic searching process. We accelerate this synthesis process using tree-structured vector quantization.

Keyphrases

tree-structured vector quantization    fast texture synthesis    random noise    texture synthesis    many application    example texture    markov random field texture model    image processing    synthesized texture    efficient algorithm    example texture patch    sample texture    previous technique    high quality result    image editing    synthesized result    computer graphic    generates texture    deterministic searching process    perceived quality    arbitrary size    temporal texture generation    realistic texture synthesis    texture generation process    synthesis process   

Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University