Two Compound Random Field Texture Models

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• 关键词：Texture ; Texture synthesis ; Compound random field model ; CAR model ; Two ; dimensional Bernoulli mixture ; Two ; dimensional Gaussian mixture ; Bidirectional texture function
• 刊名：Lecture Notes in Computer Science
• 出版年：2017
• 出版时间：2017
• 年：2017
• 卷：10125
• 期：1
• 页码：44-51
• 丛书名：Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
• ISBN：978-3-319-52277-7
• 卷排序：10125

文摘

Two novel models for texture representation using parametric compound random field models are introduced. These models consist of a set of several sub-models each having different characteristics along with an underlying structure model which controls transitions between them. The structure model is a two-dimensional probabilistic mixture model either of the Bernoulli or Gaussian mixture type. Local textures are modeled using the fully multispectral three-dimensional causal auto-regressive models. Both presented compound random field models allow to reproduce, compress, edit, and enlarge a given measured color, multispectral, or bidirectional texture function (BTF) texture so that ideally both measured and synthetic textures are visually indiscernible.