04427nam 22006135 450 991025433340332120200703013744.03-319-53994-910.1007/978-3-319-53994-2(CKB)3710000001087254(DE-He213)978-3-319-53994-2(MiAaPQ)EBC4818786(PPN)199768145(EXLCZ)99371000000108725420170306d2017 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierEdge Detection Methods Based on Generalized Type-2 Fuzzy Logic /by Claudia I. Gonzalez, Patricia Melin, Juan R. Castro, Oscar Castillo1st ed. 2017.Cham :Springer International Publishing :Imprint: Springer,2017.1 online resource (X, 89 p. 34 illus., 21 illus. in color.)SpringerBriefs in Computational Intelligence,2625-37043-319-53993-0 Includes bibliographical references and index.Introduction -- Generalized Type-2 Fuzzy Logic -- Edge Detection Methods and Filters Used on Digital Image Processing -- Metrics for Edge Detection Methods -- Edge Detection Methods Based on Generalized Type-2 Fuzzy Logic Systems -- Generalized Type-2 Fuzzy Edge Detection Applied on a Face Recognition System -- Experimentation and Results Discussion -- Conclusions.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 using a generalized type-2 fuzzy edge detector in pattern recognition applications. The main goal of using generalized type-2 fuzzy logic in edge detec-tion applications is to provide them with the ability to handle uncertainty in processing real world images; otherwise, to demonstrate that a GT2 FS has a better performance than the edge detection methods based on type-1 and type-2 fuzzy logic systems.SpringerBriefs in Computational Intelligence,2625-3704Computational intelligenceArtificial intelligencePattern recognitionComputational Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/T11014Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Pattern Recognitionhttps://scigraph.springernature.com/ontologies/product-market-codes/I2203XComputational intelligence.Artificial intelligence.Pattern recognition.Computational Intelligence.Artificial Intelligence.Pattern Recognition.006.37Gonzalez Claudia Iauthttp://id.loc.gov/vocabulary/relators/aut872836Melin Patriciaauthttp://id.loc.gov/vocabulary/relators/autCastro Juan Rauthttp://id.loc.gov/vocabulary/relators/autCastillo Oscarauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910254333403321Edge Detection Methods Based on Generalized Type-2 Fuzzy Logic1948578UNINA