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

机译:对象形状匹配小波滤波器组的系统设计,用于缺陷检测

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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:在我们以前的作品中,我们提出了用于优化小波滤波器的方法,可用于图像对象的分类。小波滤波器组被设计为双正常,这使得对给定图像数据能够进行多尺度分析。此外,滤波器相对于形状进行了优化,这有助于过滤器组继承物体的属性。只有在所谓的对象过滤器的帮助下,才能实现该优化,该滤波器旨在具有每个类的典型对象的曲线。与先前的作品相比,手动设计了物体过滤器,本文介绍了对物体过滤器的系统和自动设计方法。新设计的过滤器用于优化小波滤波器组以进行分类问题。通过基于先前使用的对象滤波器将结果与小波滤波器组的结果进行比较来完成该方法的评估。

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