Logic

Edge Detection Methods Based on Generalized Type-2 Fuzzy by Claudia I. Gonzalez, Patricia Melin, Juan R. Castro, Oscar

By Claudia I. Gonzalez, Patricia Melin, Juan R. Castro, Oscar Castillo

In this e-book 4 new tools are proposed. within the first approach the generalized type-2 fuzzy good judgment is mixed with the morphological gra-dient method. the second one technique combines the overall type-2 fuzzy platforms (GT2 FSs) and the Sobel operator; within the 3rd technique the me-thodology in line with Sobel operator and GT2 FSs is greater to be utilized on colour photos. within the fourth strategy, we proposed a singular side detec-tion strategy the place, a electronic snapshot is switched over a generalized type-2 fuzzy picture. during this publication it's also integrated a comparative examine of type-1, inter-val type-2 and generalized type-2 fuzzy structures as instruments to reinforce side detection in electronic pictures whilst utilized in conjunction with the morphologi-cal gradient and the Sobel operator. The proposed generalized type-2 fuzzy side detection equipment have been proven with benchmark pictures and artificial photographs, in a grayscale and colour format.
Another contribution during this ebook is that the generalized type-2 fuzzy aspect detector strategy is utilized within the preprocessing part of a face rec-ognition approach; the place the popularity approach relies on a monolithic neural community. the purpose of this a part of the e-book is to teach the good thing about utilizing a generalized type-2 fuzzy area detector in trend reputation applications.
The major objective of utilizing generalized type-2 fuzzy common sense in facet detec-tion functions is to supply them having the ability to deal with uncertainty in processing genuine global photos; differently, to illustrate GT2 FS has a greater functionality than the sting detection tools according to type-1 and type-2 fuzzy good judgment systems.

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Extra resources for Edge Detection Methods Based on Generalized Type-2 Fuzzy Logic

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2 Input membership functions using in the GT2 FS edge detection The previous fuzzy rules are used to infer the gray tone of each pixel for the image with the following reasoning. In the first rule when the horizontal gradient gx and vertical gradient gy are low means that there is no difference between the gray tones in its neighbors pixels, then the output pixel must belong to a homogeneous or not edges region, and then the output pixel is high. 1 Generalized Type-2 Fuzzy Edge Detection Method … 39 Fig.

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.

The inference system has one output S (the edge), the linguistic values used for the output are: edge and no_edge, and we selected the range [0, 1], since the input image was normalized in this range, where the minimum value for the output is represented by Eq. 7) and maximum by Eq. 8). The Gaussian membership functions for the output are obtained with Eqs. 16), the means of each function are obtained with Eq. 11) and the r value with Eq. 9). Obtain the FOU for the output. The FOU for the output variable S, was calculated in a similar way to the inputs variables.

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