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Review and Outlook for Texture Analysis Methods

机译:纹理分析方法的审查和展望

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摘要

The description and analysis of textures is a widely discussed topic. Different methods have already been developed but there are still a lot of opportunities to develop new approaches. For this reason, in this report at first an overview of the standard methods for the analysis of textures is given. Based on that, new ideas and opportunities are presented which extend these methods but also represent totally new approaches. In the field of structuralstatistical textures the change in the structural arrangement scheme is described analogously to the modulation of signals in communications technology. A basic fundament is the representation of an image signal by a two-dimensional extended Fourier series whose parameters can be obtained using unmodulated texture primitives. Another subject is the determination of parameters in the modeling of textures using AR-models. This estimate is carried out using the Support Vector Regression (SVR) and, thus, offers an alternative to the in the field of texture analysis widely used Least-Square (LS) and Maximum-Likelihood (ML) estimation methods. In the field of optical inspection of textiles an approach will be introduced, which enables the assessment of tissue properties and the detection of errors. The assessment is not based on the derivation of features from the methods of texture analysis, but uses the possibilities of the image acquisition by a variable illumination.
机译:纹理的描述和分析是一个广泛讨论的主题。已经开发了不同的方法,但仍有很多机会开发新方法。出于这个原因,在本报告中首先概述了对纹理分析分析的标准方法。根据该方法,延长了这些方法的新思路和机会,还表示完全新的方法。在结构实地的领域中,结构布置方案的变化类似于通信技术中信号的调制。基本基础是由二维扩展傅里叶系列的图像信号的表示,其参数可以使用未调制的纹理基元获得。另一个主题是使用AR模型确定纹理建模中的参数。使用支持向量回归(SVR)进行该估计,因此,提供纹理分析领域的替代方案,广泛使用最小二乘(LS)和最大似然(ML)估计方法。在纺织品的光学检查领域,将引入一种方法,这使得能够评估组织性质和检测误差。评估不是基于来自纹理分析方法的特征的推导,但是使用可变照明的图像采集的可能性。

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