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首页> 外文期刊>International journal of image and data fusion >Texture characterization, representation, description, and classification based on full range Gaussian Markov random field model with Bayesian approach
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Texture characterization, representation, description, and classification based on full range Gaussian Markov random field model with Bayesian approach

机译：基于贝叶斯方法的全范围高斯马尔可夫随机场模型的纹理表征，表示，描述和分类

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摘要

A statistical approach, based on full range Gaussian Markov random field model, is proposed for texture analysis such as texture characterization, unique representation, description, and classification. The parameters of the model are estimated based on the Bayesian approach. The estimated parameters are utilized to compute autocorrelation coefficients. The computed autocorrelation coefficients fall in between -1 and +1. The coefficients are converted into decimal numbers using a simple transformation. Based on the decimal numbers, two texture descriptors are proposed: (ⅰ) texnum, the local descriptor; (ⅱ) texspectrum, the global descriptor. The decimal numbers are proposed to represent the textures present in a small image region. These numbers uniquely represent the texture primitives. The textured image under analysis is represented globally by observing the frequency of occurrences of the texnums called texspectrum. The textures are identified and are distinguished from untextured regions with edges. The classification analyses such as supervised and unsupervised are performed on the local descriptors.
机译：提出了一种基于全范围高斯马尔可夫随机场模型的统计方法，用于纹理分析，例如纹理表征，唯一表示，描述和分类。该模型的参数是基于贝叶斯方法估计的。估计的参数用于计算自相关系数。计算的自相关系数在-1和+1之间。使用简单的转换将系数转换为十进制数。基于十进制数，提出了两个纹理描述符：（ⅰ）texnum，局部描述符； （ⅱ）texspectrum，全局描述符。建议使用十进制数字表示小图像区域中存在的纹理。这些数字唯一表示纹理基元。通过观察被称为texspectrum的texnum的出现频率，可以整体表示待分析的纹理图像。识别出纹理并将其与带有边缘的未纹理化区域区分开。对局部描述符执行分类分析，例如监督和非监督。

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