• Documents
  • Authors
  • Tables
  • Other Seers ▼
    RefSeer AckSeer CollabSeer SeerSeer
  • Log in
  • Sign up
  • MetaCart

CiteSeerX logo

Advanced Search Include Citations
Advanced Search Include Citations | Disambiguate

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]
  • [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:354 - 7 self
  • Summary
  • Active Bibliography
  • Co-citation
  • Clustered Documents
  • Version History

Versions

  • Version 0
  • Version 1

Version History

Metadata Version 1

DatumValueSource
TITLE Fast texture synthesis using tree-structured vector quantization INFERENCE
AUTHOR NAME Li-yi Wei SVM HeaderParse 0.2
AUTHOR AFFIL Stanford University SVM HeaderParse 0.2
AUTHOR NAME Marc Levoy SVM HeaderParse 0.2
AUTHOR AFFIL Stanford University SVM HeaderParse 0.2
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. SVM HeaderParse 0.2
YEAR 2000 INFERENCE
VENUE TYPE CONFERENCE INFERENCE
PAGES 479--488 INFERENCE
CITATIONS 28 found ParsCit 1.0
The National Science Foundation
  • About CiteSeerX
  • Submit Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

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

© 2007-2010 The Pennsylvania State University