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Accelerated Parallel Texture Optimization

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Abstract

Texture optimization is a texture synthesis method that can efficiently reproduce various features of exemplar textures. However, its slow synthesis speed limits its usage in many interactive or real time applications. In this paper, we propose a parallel texture optimization algorithm to run on GPUs. In our algorithm, k-coherence search and principle component analysis (PCA) are used for hardware acceleration, and two acceleration techniques are further developed to speed up our GPU-based texture optimization. With a reasonable precomputation cost, the online synthesis speed of our algorithm is 4000+ times faster than that of the original texture optimization algorithm and thus our algorithm is capable of interactive applications. The advantages of the new scheme are demonstrated by applying it to interactive editing of flow-guided synthesis.

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Authors and Affiliations

  1. State Key Lab of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, 100080, China

    Hao-Da Huang & Wen-Cheng Wang

  2. Microsoft Research Asia, Beijing, 100080, China

    Xin Tong

  3. Graduate University of Chinese Academy of Sciences, Beijing, 100080, China

    Hao-Da Huang

Authors
  1. Hao-Da Huang
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  2. Xin Tong
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  3. Wen-Cheng Wang
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Corresponding author

Correspondence to Hao-Da Huang.

Additional information

The IOS authors are partially supported by the National High Technology Development 863 Program of China under Grant No. 2006AA01Z306 and the National Grand Fundamental Research 973 Program of China under Grant No. 2002CB312102.

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Huang, HD., Tong, X. & Wang, WC. Accelerated Parallel Texture Optimization. J Comput Sci Technol 22, 761–769 (2007). https://doi.org/10.1007/s11390-007-9083-x

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  • DOI: https://doi.org/10.1007/s11390-007-9083-x

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