Search - synthesis texture markov random field

Search - synthesis texture markov random field - List

[Special Effects] Example-Based_Automatic_Portraiture DL : 0: 摘　要　提出了一种基于样本学习的人脸肖像画自动生成算法.文章采用非均匀的马尔科夫随机场模型来描述肖像画与人脸图像之间的统计关系 ,并使用基于训练样本的非参数化的概率表示 ,在贝叶斯优化的框架下设计了迭代采样算法 ,可以自动的从人脸图像生成特定风格的肖像画.在该方法中 ,使用非均匀的统计模型是保持肖像中人脸结构准确性的关键.文中所提供的例子表明了该文方法的有效性-Abstract　In this paper , we present a new approach for automatically generating a life2like port rait f rom a f rontal face image. We learn the port raiture f rom a set of real artwork examples. Different f rom previous texture synthesis and image synthesis works that assumed modeling is homogeneous , Inhomo2 geneous Markov Random Field Model is employed as the statistical model , and a non2 paramet ric sam2 pling scheme is used to capture the complex statistical characteristics of face image and corresponding artist drawing in this paper . In our st rategy , only those pixels corresponding to a port rait point are sampled. Such a st rategy is crucial for maintaining facial st ructure and guaranteeing coherence of por2 t rait lines. Experimental result s demonst rate the effectiveness and life 2likeness of our approach.
Update : 2025-03-07 Size : 276kb Publisher : alsocc
[Bio-Recognize] Fifield-RemoteOperatingSystemDetection DL : 0: A non-parametric method for texture synthesis proposed. The texture synthesis process grows a new image outward from an initial seed, one pixel at a time. A Markov random field model is assumed, and the conditional distribution of a pixel given all its neighbors synthesized so far is estimated by querying the sample image and finding all similar neighborhoods. The degree of randomness is controlled by a single perceptually intuitive parameter-A non-parametric method for texture synthesis is proposed. The texture synthesis process grows a new image outward from an initial seed, one pixel at a time. A Markov random field model is assumed, and the conditional distribution of a pixel given all its neighbors synthesized so far is estimated by querying the sample image and finding all similar neighborhoods. The degree of randomness is controlled by a single perceptually intuitive parameter
Update : 2025-03-07 Size : 88kb Publisher : maulik
[matlab] HuntLinKulkarni-PredictingCourseGrades DL : 0: 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.
Update : 2025-03-07 Size : 105kb Publisher : maulik