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Titlebook: Edge Detection Methods Based on Generalized Type-2 Fuzzy Logic; Claudia I. Gonzalez,Patricia Melin,Oscar Castillo Book 2017 The Author(s)

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發(fā)表于 2025-3-21 16:34:58 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Edge Detection Methods Based on Generalized Type-2 Fuzzy Logic
編輯Claudia I. Gonzalez,Patricia Melin,Oscar Castillo
視頻videohttp://file.papertrans.cn/303/302238/302238.mp4
概述Includes a comparative study of type-1, interval type-2 and generalized type-2 fuzzy systems as tools to enhance edge detection in digital images when used in conjunction with the morphological gradie
叢書名稱SpringerBriefs in Applied Sciences and Technology
圖書封面Titlebook: Edge Detection Methods Based on Generalized Type-2 Fuzzy Logic; Claudia I. Gonzalez,Patricia Melin,Oscar Castillo Book 2017 The Author(s)
描述In this book four new methods are proposed. In the first method the generalized type-2 fuzzy logic is combined with the morphological gra-dient technique. The second method combines the general type-2 fuzzy systems (GT2 FSs) and the Sobel operator; in the third approach the me-thodology based on Sobel operator and GT2 FSs is improved to be applied on color images. In the fourth approach, we proposed a novel edge detec-tion method where, a digital image is converted a generalized type-2 fuzzy image. In this book it is also included a comparative study of type-1, inter-val type-2 and generalized type-2 fuzzy systems as tools to enhance edge detection in digital images when used in conjunction with the morphologi-cal gradient and the Sobel operator. The proposed generalized type-2 fuzzy edge detection methods were tested with benchmark images and synthetic images, in a grayscale and color format..Another contribution in this book is that the generalized type-2 fuzzy edge detector method is applied in the preprocessing phase of a face rec-ognition system; where the recognition system is based on a monolithic neural network. The aim of this part of the book is to show the advantage of u
出版日期Book 2017
關(guān)鍵詞Computational Intelligence; Pattern Recognition; Edge Detection Methods; Face Recognition; Digital Image
版次1
doihttps://doi.org/10.1007/978-3-319-53994-2
isbn_softcover978-3-319-53993-5
isbn_ebook978-3-319-53994-2Series ISSN 2191-530X Series E-ISSN 2191-5318
issn_series 2191-530X
copyrightThe Author(s) 2017
The information of publication is updating

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This Chapter presents some available metrics to measure the quality of the edge detection methods. We describe the figure of merit of Pratt (FOM) and the quality measurement using the MSE, PSNR and SSIM indices. We also include the equations to calculate these metrics.
地板
發(fā)表于 2025-3-22 07:49:43 | 只看該作者
This Chapter consists in two parts; first we are presenting the methodology to develop a GT2 fuzzy edge detection method, and in this proposed method we are including the low-pass and high-pass filters. Secondly, the fuzzy edge detector is applied on a face recognition system?using a monolithic Neural Network.
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發(fā)表于 2025-3-22 12:17:26 | 只看該作者
Generalized Type-2 Fuzzy Logic,This Chapter describes the basic concepts about generalized type-2 fuzzy sets theory. We explain the generalized type-2 fuzzy system approximation based on α-planes including the fuzzifier process, fuzzy rules, inference engine, type reducer and defuzzification process; all these definitions are used to develop the fuzzy edge detection methods
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發(fā)表于 2025-3-22 14:35:46 | 只看該作者
Metrics for Edge Detection Methods,This Chapter presents some available metrics to measure the quality of the edge detection methods. We describe the figure of merit of Pratt (FOM) and the quality measurement using the MSE, PSNR and SSIM indices. We also include the equations to calculate these metrics.
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SpringerBriefs in Applied Sciences and Technologyhttp://image.papertrans.cn/e/image/302238.jpg
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