By Claudia I. Gonzalez, Patricia Melin, Juan R. Castro, Oscar Castillo
In this ebook 4 new tools are proposed. within the first approach the generalized type-2 fuzzy common sense is mixed with the morphological gra-dient procedure. the second one approach combines the overall type-2 fuzzy structures (GT2 FSs) and the Sobel operator; within the 3rd procedure the me-thodology in accordance with Sobel operator and GT2 FSs is more desirable to be utilized on colour photos. within the fourth method, we proposed a unique aspect detec-tion technique the place, a electronic photo is switched over a generalized type-2 fuzzy photo. during this ebook it's also incorporated a comparative learn of type-1, inter-val type-2 and generalized type-2 fuzzy structures as instruments to reinforce area detection in electronic photographs while utilized in conjunction with the morphologi-cal gradient and the Sobel operator. The proposed generalized type-2 fuzzy facet detection tools have been proven with benchmark photographs and artificial photographs, in a grayscale and colour format.
Another contribution during this ebook is that the generalized type-2 fuzzy area detector strategy is utilized within the preprocessing part of a face rec-ognition method; the place the popularity approach relies on a monolithic neural community. the purpose of this a part of the ebook is to teach the benefit of utilizing a generalized type-2 fuzzy aspect detector in trend attractiveness applications.
The major aim of utilizing generalized type-2 fuzzy good judgment in aspect detec-tion functions is to supply them being able to deal with uncertainty in processing genuine international photos; differently, to illustrate GT2 FS has a greater functionality than the sting detection equipment in accordance with type-1 and type-2 fuzzy good judgment systems.
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Additional info for Edge Detection Methods Based on Generalized Type-2 Fuzzy Logic
9321 is obtained. Based on these results it can be noted that the edge detector obtained using IT2 FSs is better that the edge detector obtained with T1 FSs. In the example of Fig. 3, the changes on the IT2 membership functions with different values of the FOU are shown. 8 FOU 1 HIGH EDGE Fig. 1. A sample of the GT2 membership functions with different FOU value which are considered for this case study are shown in Fig. 4. 9618. 2 Generalized Type-2 Fuzzy Systems Combined with the Sobel Operator 53 Fig.
2 that for these images, we do not have the ideal edges or reference images, which are important to calculate the FOM metric; for this reason, in these experiments ﬁrst the ideal edges were obtained using the traditional Morphological gradient technique; after that we applied Gaussian noise on all images and ﬁnally the output edges are calculated with the fuzzy edge detection methods. 2. 9733). 9839) were better that the MG + IT2 FSs; the main reason is that the uncertainty in edge detection is modeled more closely with GT2 FSs; in other words, the GT2 FSs allows for better modeling of uncertainty, because it gives more degrees of freedom in comparison to T1 and IT2 FSs.
9. It can be observed that the measurements obtained with the FOM given by Eq. 9606 is obtained. As mentioned previously, a FOM close to one means that the detected edge has good quality. 9 is such that as the FOU factor is increased, the values of the FOM also increase. These changes can be explained as follows: different FOU’s represent different levels of uncertainty and there should be an optimal level of uncertainty for modeling the image; however, after the execution of the ten experiments, with a FOU factor of one, the FOM value was lower, and this means that the problem reaches its point of generalization and does not need more uncertainty level or has reached its optimum level.
Edge Detection Methods Based on Generalized Type-2 Fuzzy Logic by Claudia I. Gonzalez, Patricia Melin, Juan R. Castro, Oscar Castillo