文件名称:HuntLinKulkarni-PredictingCourseGrades
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Most recent approaches have posed texture synthesis in a
statistical setting as a problem of sampling from a probability
distribution. Zhu et. al. [12] model texture as a Markov
Random Field and use Gibbs sampling for synthesis. Unfortunately,
Gibbs sampling is notoriously slow and in fact
it is not possible to assess when it has converged. Heeger
and Bergen [6] try to coerce a random noise image.
statistical setting as a problem of sampling from a probability
distribution. Zhu et. al. [12] model texture as a Markov
Random Field and use Gibbs sampling for synthesis. Unfortunately,
Gibbs sampling is notoriously slow and in fact
it is not possible to assess when it has converged. Heeger
and Bergen [6] try to coerce a random noise image.
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HuntLinKulkarni-PredictingCourseGrades.pdf