2 D autoregressive model for texture analysis and synthesis

D. Vaishali, R. Ramesh, J. Christaline
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引用次数: 9

Abstract

Spatial autoregressive (AR) models have been extensively used to represent texture images in machine learning applications. This work emphasizes the contribution of 2D autoregressive models for analysis and synthesis of textural images. Autoregressive model parameters as a feature set of texture image represent texture and used for synthesis. Yule walker Least Square (LS) method has used for parameter estimation. The test statistics for choice of proper neighbourhood (N) has also been suggested. The Brodatz texture image album has chosen for the experimentation. Parameters have estimated from the textures. The test statistics decides the best neighbourhood or proper order of the model. The synthesized texture image and the original texture image have compared for perceptual similarities. It is been inferred that the proper neighbourhood for a given texture is unique and solely depends on the properties of the texture.
纹理分析与合成的二维自回归模型
空间自回归(AR)模型在机器学习应用中被广泛用于表示纹理图像。这项工作强调了二维自回归模型对纹理图像分析和合成的贡献。自回归模型参数作为纹理图像的特征集代表纹理,用于纹理合成。采用Yule walker最小二乘(LS)方法进行参数估计。给出了适当邻域(N)选择的检验统计量。实验选择了Brodatz纹理图集。从纹理中估计参数。检验统计量决定模型的最佳邻域或适当的顺序。将合成的纹理图像与原始纹理图像进行感知相似性比较。可以推断,给定纹理的适当邻域是唯一的,并且完全取决于纹理的属性。
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