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020 _a9783030129316
024 7 _a10.1007/978-3-030-12931-6
_2doi
040 _bspa
_dES-MaUEC
_cES-MaUEC
050 4 _aQ342
_b2019 EB
100 1 _aOliva, Diego.
_eautor
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aMetaheuristic Algorithms for Image Segmentation :
_bTheory and Applications
_cby Diego Oliva, Mohamed Abd Elaziz, Salvador Hinojosa.
264 1 _aCham
_bSpringer International Publishing :
_bImprint: Springer
_c2019.
300 _a 1 recurso en línea (XV, 226 páginas)
_b58 illus., 43 illus. in color)
336 _2rdacontent
_aTexto
_btxt
337 _2rdamedia
_asin mediación
_bn
338 _2rdacarrier
_avolumen
_bnc
347 _atext file
_bPDF
_2rda
490 0 _aStudies in Computational Intelligence
_x1860-949X ;
_v825
490 0 _aIntelligent Technologies and Robotics (Springer-42732)
505 0 _aIntroduction -- Optimization -- Metaheuristic optimization -- Image processing -- Image Segmentation using metaheuristics -- Multilevel thresholding for image segmentation based on metaheuristic Algorithms -- Otsu's between class variance and the tree seed algorithm -- Image segmentation using Kapur's entropy and a hybrid optimization algorithm -- Tsallis entropy for image thresholding -- Image segmentation with minimum cross entropy -- Fuzzy entropy approaches for image segmentation -- Image segmentation by gaussian mixture -- Image segmentation as a multiobjective optimization problem -- Clustering algorithms for image segmentation -- Contextual information in image thresholding.
520 3 _aThis book presents a study of the most important methods of image segmentation and how they are extended and improved using metaheuristic algorithms. The segmentation approaches selected have been extensively applied to the task of segmentation (especially in thresholding), and have also been implemented using various metaheuristics and hybridization techniques leading to a broader understanding of how image segmentation problems can be solved from an optimization perspective. The field of image processing is constantly changing due to the extensive integration of cameras in devices; for example, smart phones and cars now have embedded cameras. The images have to be accurately analyzed, and crucial pre-processing steps, like image segmentation, and artificial intelligence, including metaheuristics, are applied in the automatic analysis of digital images. Metaheuristic algorithms have also been used in various fields of science and technology as the demand for new methods designed to solve complex optimization problems increases. This didactic book is primarily intended for undergraduate and postgraduate students of science, engineering, and computational mathematics. It is also suitable for courses such as artificial intelligence, advanced image processing, and computational intelligence. The material is also useful for researches in the fields of evolutionary computation, artificial intelligence, and image processing.
988 _aPrimersemestre_2019_Robotics
650 7 _2embne
_aInteligencia artificial
_9413115
700 1 _aAbd Elaziz, Mohamed.
_eautor
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aHinojosa, Salvador.
_eautor
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
776 0 8 _iPrinted edition:
_z9783030129309
776 0 8 _iPrinted edition:
_z9783030129323
776 0 8 _iPrinted edition:
_z9783030129330
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-12931-6
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
_cLE
998 _dz
_feng
_ggw
_h0