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Characteristic extraction from an ambiguous image using fuzzy mathematical morphology with adaptive structuring elements

机译:利用自适应结构元素模糊数学形态的模糊数学形态学的特征提取

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Fuzzy Mathematical Morphology (FMM) is proposed as a new method of the image processing, especially the analysis of image characteristics. FMM works with two images: The original image to be processed and a structuring element. However, the shape and value of structuring element must decide before image processing. Also there is the problem that the image changes largely by the selection of the structuring element. In this paper, we propose a new filter to extract characteristics in an ambiguous image. First, the adaptive structuring elements (ASE) are defined. The ASE can be determined directly from the input image without any arbitrary choice, which is sensitivity to ambiguous images. Next, we propose the Dynamic Fuzzy Morphological Filter (DFMF) with ASE. This method is useful for automatic inspection systems using image processing and effective to characteristics extraction. Performance evaluation via simulations shows that the DFMF efficiently extracts characteristics. Furthermore, the DFMF shows a good performance compared with the other filters.
机译:建议模糊数学形态(FMM)作为图像处理的新方法,尤其是图像特征的分析。 FMM适用于两个图像:要处理的原始图像和结构元素。但是,结构元素的形状和值必须在图像处理之前决定。此外,存在的问题是图像在很大程度上通过选择构造元素而变化。在本文中,我们提出了一种新的过滤器来提取模糊图像中的特性。首先,定义自适应结构元素(ASE)。可以直接从输入图像确定ASE而没有任何任意选择,这是对模糊图像的敏感性。接下来,我们提出了具有ASE的动态模糊形态过滤器(DFMF)。该方法对于使用图像处理的自动检查系统有用,并且有效地提取。通过仿真进行性能评估表明,DFMF有效提取特性。此外,与其他过滤器相比,DFMF显示出良好的性能。

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