Abstract
A type-2 fuzzy edge detection method is presented in this paper. The general process consists of first obtaining the image gradients in the four directions—horizontal, vertical, and the two diagonals—and this technique is known as the morphological gradient. After that, the general type-2 fuzzy Sugeno integral (GT2 FSI) is used to integrate the four image gradients. In this second step, the GT2 FSI establishes criteria to determine at which level the obtained image gradient belongs to an edge during the process; this is calculated assigning different general type-2 fuzzy densities, and these fuzzy gradients are aggregated using the meet and join operators. The gradient integration using the GT2 FSI provides a methodology for achieving more robust edge detection, even more if we are working with blurry images. The experimental evaluations are performed on synthetic and real images, and the accuracy is quantified using Pratt’s Figure of Merit. The results values demonstrate that the proposed edge detection method outperforms other existing algorithms.
Highlights
The edge detection process is widely used in pattern recognition and computer vision systems because it is helpful in obtaining satisfactory results when applied in the preprocessing phase.the process of finding the edges is not easy, especially if the image is blurry or distorted by noise; these phenomena frequently occurs when the image is captured by any acquisition hardware, factors like the distance, quality, and resolution of cameras, environment, and illumination variation tend to produce images with ambiguous or incomplete data
The results of the proposed fuzzy edge detection method are presented below, the implementation of which consists in the integration of the image gradients using the integral Sugeno combined with the general type-2 (GT2) fuzzy sets (FS) operators
The visual results of the edge detection for the synthetic images database are shown in Table 9; where the last column represents the value of the fuzzy density associated with each gradient
Summary
The edge detection process is widely used in pattern recognition and computer vision systems because it is helpful in obtaining satisfactory results when applied in the preprocessing phase.the process of finding the edges is not easy, especially if the image is blurry or distorted by noise; these phenomena frequently occurs when the image is captured by any acquisition hardware, factors like the distance, quality, and resolution of cameras, environment, and illumination variation tend to produce images with ambiguous or incomplete data. The issue of determining what is an image edge and what is not becomes more critical by the fact that edges are partially distorted or hidden. In order to solve this issue for image edge detection, in recent years, various approaches that involve soft computing methods have been put forward, including the principles of fuzzy set theory, which is one of the main topics in this work. Fuzzy techniques for edge detection have gained importance because they offer a good alternative to enhance the accuracy in the edge detection process. We can mention some important contributions for edge detection using type-1 (T1), interval type-2 (IT2), interval-valued and general type-2 (GT2) fuzzy sets (FS)
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