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Edge Detection Approach Based on Type-2 Fuzzy Images

机译:基于2型模糊图像的边缘检测方法

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This paper presents a new fuzzy edge detection method applied on fuzzy images. The aim of this approach is that each pixel value in a digital image can be extended to be a fuzzy number; therefore, the images can be fuzzified using interval type-2 (IT2 FS), general type-2 (GT2 FS) and type-1 fuzzy sets (T1 FS). In an image processing system when an image is captured by any acquisition hardware, there are diverse factors that could introduce noise to the image (distance, environment and lighting) and consequently add uncertainty, varying the brightness or color information; so, in this approach the idea is to have a better handling of the imprecision that could exist in each numerical pixel.Due to the fact that we are not sure if each pixel value is precise, we can add a level of uncertainty in the image processing and handle each crisp pixel as a fuzzy pixel. We are presenting results using different types of membership functions (MFs), Triangular and Trapezoidal for the T1 and IT2 FS. For the GT2 FS we use two types of MFs: Triangular on the primary MY and Gaussian in the secondary MFs and Trapezoidal on the primary MY and Gaussian in the secondary MFs. The parameters for the IT2 and GT2 MFs are obtained using a Genetic Algorithm. According with the results, we provide statistical evidence to confirm that the results achieved by the GT2 FS have a significant advantage with respect to the T1 and IT2 FS.
机译:本文介绍了一种在模糊图像上应用了一种新的模糊边缘检测方法。这种方法的目的是数字图像中的每个像素值可以扩展为模糊数;因此,可以使用间隔类型-2(IT2 FS),一般类型-2(GT2 FS)和类型-1模糊集(T1 FS)来模糊图像。在图像处理系统中,当任何采集硬件捕获图像时,有不同的因素可以引入图像(距离,环境和照明),从而增加不确定性,改变亮度或颜色信息;因此,在这种方法中,这个想法是更好地处理每个数值像素中可能存在的不精确。到我们不确定每个像素值是否精确的事实,我们可以在图像中添加一个不确定性级别处理和处理每个清晰的像素作为模糊像素。我们正在使用不同类型的隶属函数(MFS),三角形和梯形的结果,用于T1和IT2 FS。对于GT2 FS,我们使用两种类型的MFS:在次级MFS和高斯的次级MF和高斯的主要My和高斯的三角形上,在二级MF中的主要我和高斯。使用遗传算法获得IT2和GT2 MFS的参数。根据结果​​,我们提供统计证据证实,通过GT2 FS实现的结果对于T1和IT2 FS具有显着的优势。

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