LEADER 04454nam 22006135 450 001 9910254333403321 005 20200703013744.0 010 $a3-319-53994-9 024 7 $a10.1007/978-3-319-53994-2 035 $a(CKB)3710000001087254 035 $a(DE-He213)978-3-319-53994-2 035 $a(MiAaPQ)EBC4818786 035 $a(PPN)199768145 035 $a(EXLCZ)993710000001087254 100 $a20170306d2017 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEdge Detection Methods Based on Generalized Type-2 Fuzzy Logic$b[electronic resource] /$fby Claudia I. Gonzalez, Patricia Melin, Juan R. Castro, Oscar Castillo 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (X, 89 p. 34 illus., 21 illus. in color.) 225 1 $aSpringerBriefs in Computational Intelligence,$x2625-3704 311 $a3-319-53993-0 320 $aIncludes bibliographical references and index. 327 $aIntroduction -- 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. 330 $aIn this book four new methods are proposed. In the first method the generalized type-2 fuzzy logic is combined with the morphological gra-dient technique. The second method combines the general type-2 fuzzy systems (GT2 FSs) and the Sobel operator; in the third approach the me-thodology based on Sobel operator and GT2 FSs is improved to be applied on color images. In the fourth approach, we proposed a novel edge detec-tion method where, a digital image is converted a generalized type-2 fuzzy image. In this book it is also included a comparative study of type-1, inter-val type-2 and generalized type-2 fuzzy systems as tools to enhance edge detection in digital images when used in conjunction with the morphologi-cal gradient and the Sobel operator. The proposed generalized type-2 fuzzy edge detection methods were tested with benchmark images and synthetic images, in a grayscale and color format. Another contribution in this book is that the generalized type-2 fuzzy edge detector method is applied in the preprocessing phase of a face rec-ognition system; where the recognition system is based on a monolithic neural network. The aim of this part of the book is to show the advantage of using a generalized type-2 fuzzy edge detector in pattern recognition applications. The main goal of using generalized type-2 fuzzy logic in edge detec-tion applications is to provide them with the ability to handle uncertainty in processing real world images; otherwise, to demonstrate that a GT2 FS has a better performance than the edge detection methods based on type-1 and type-2 fuzzy logic systems. 410 0$aSpringerBriefs in Computational Intelligence,$x2625-3704 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aPattern recognition 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aPattern recognition. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aPattern Recognition. 676 $a006.37 700 $aGonzalez$b Claudia I$4aut$4http://id.loc.gov/vocabulary/relators/aut$0872836 702 $aMelin$b Patricia$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aCastro$b Juan R$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aCastillo$b Oscar$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254333403321 996 $aEdge Detection Methods Based on Generalized Type-2 Fuzzy Logic$91948578 997 $aUNINA