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Feature Extraction with an Associative Neural Network and Its Application in Industrial Quality Control

机译：联想神经网络特征提取及其在工业质量控制中的应用

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摘要

There are several approaches to quality control in industrial processes. This work is center in artificial vision applications for defect detection and its classification and control. In particular, we are center in textile fabric and the use of texture analysis for discrimination and classification. Most previous methods have limitations in accurate discrimination or complexity in time calculation; so we apply parallel and signal processing techniques. Our algorithm is divided in two phases: a first phase is the extraction of texture features and later we classify it. Texture features should have the followings properties: be invariant under the transformations of translation, rotation, and scaling; a good discriminating power; and take the non-stationary nature of texture account. In Our approach we use Orthogonal Associative Neural Networks to Texture identification and extraction of features with the previous properties. It is used in the feature extraction and classification phase (where its energy function is minimized) too, so all the method was applying to defect detection in textile fabric. Several experiments has been done comparing the proposed method with other paradigms. In response time and quality of response our proposal gets the best parameters.
机译：工业过程中有几种质量控制方法。这项工作是人工视觉应用中缺陷检测及其分类和控制的中心。特别是，我们在纺织面料和使用质地分析进行区分和分类方面处于中心地位。先前的大多数方法在准确判别或时间计算复杂性方面都有局限性。因此我们应用并行和信号处理技术。我们的算法分为两个阶段：第一个阶段是纹理特征的提取，然后我们对其进行分类。纹理特征应具有以下属性：在平移，旋转和缩放的转换下不变；良好的辨别力；并考虑纹理的非平稳性。在我们的方法中，我们使用正交联想神经网络对具有先前属性的特征进行纹理识别和特征提取。它也用于特征提取和分类阶段（在此阶段，其能量函数被最小化），因此所有方法都适用于织物的缺陷检测。已经进行了一些实验，将提出的方法与其他范例进行了比较。在响应时间和响应质量方面，我们的建议获得了最佳参数。

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