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Systematic design of object shape matched wavelet filter banks for defect detection

机译:目标形状匹配小波滤波器组的缺陷检测系统设计

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In our previous works, we have presented methods for optimizing wavelet filter banks, which can be used for classification of image objects. The wavelet filter banks were designed to be biorthogonal, which enables a multiscale analysis on given image data. Moreover, the filters were optimized with respect to the shape, which helps the filter banks to inherit the property of the objects. This optimization is only possible with the help of so called object filters designed to have the curve of typical objects of each class. In contrast to previous works where object filters were designed manually, a systematic and automatic design method for object filters is introduced in this paper. The new designed filters were used to optimize wavelet filter banks for classification problems. The evaluation of this method was done by comparing the results with the ones of wavelet filter banks based on the previously used object filters.
机译:在我们以前的工作中,我们提出了优化小波滤波器组的方法,这些方法可用于图像对象的分类。小波滤波器组被设计为双正交的,从而可以对给定的图像数据进行多尺度分析。此外,针对形状对滤镜进行了优化,这有助于滤镜库继承对象的属性。这种优化只有在所谓的对象过滤器的帮助下才能实现,该对象过滤器设计为具有每个类的典型对象的曲线。与以前的手动设计对象过滤器的工作相反,本文介绍了一种系统且自动的对象过滤器设计方法。新设计的滤波器用于针对分类问题优化小波滤波器组。通过将结果与基于先前使用的对象滤波器的小波滤波器组的结果进行比较,来评估该方法。

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