Texture optimization for example-based synthesis

  title={Texture optimization for example-based synthesis},
  author={Vivek Kwatra and Irfan Essa and Aaron Bobick and Nipun Kwatra},
  booktitle={SIGGRAPH '05},
We present a novel technique for texture synthesis using optimization. We define a Markov Random Field (MRF)-based similarity metric for measuring the quality of synthesized texture with respect to a given input sample. This allows us to formulate the synthesis problem as minimization of an energy function, which is optimized using an Expectation Maximization (EM)-like algorithm. In contrast to most example-based techniques that do region-growing, ours is a joint optimization approach that… CONTINUE READING
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The EM algorithm and extensions

  • Wiley series in probability and statistics. John…
  • 1997
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