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008 170306s2017 sz | s |||| 0|eng d
020 _a9783319539942
_9978-3-319-53994-2
024 7 _a10.1007/978-3-319-53994-2
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aGonzalez, Claudia I.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_961579
245 1 0 _aEdge Detection Methods Based on Generalized Type-2 Fuzzy Logic
_h[electronic resource] /
_cby Claudia I. Gonzalez, Patricia Melin, Juan R. Castro, Oscar Castillo.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aX, 89 p. 34 illus., 21 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Computational Intelligence,
_x2625-3712
505 0 _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.
520 _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.
650 0 _aComputational intelligence.
_97716
650 0 _aArtificial intelligence.
_93407
650 0 _aPattern recognition systems.
_93953
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aAutomated Pattern Recognition.
_931568
700 1 _aMelin, Patricia.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_961580
700 1 _aCastro, Juan R.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_961581
700 1 _aCastillo, Oscar.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_961582
710 2 _aSpringerLink (Online service)
_961583
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319539935
776 0 8 _iPrinted edition:
_z9783319539959
830 0 _aSpringerBriefs in Computational Intelligence,
_x2625-3712
_961584
856 4 0 _uhttps://doi.org/10.1007/978-3-319-53994-2
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c80786
_d80786