01279nam 2200361Ia 450 991069874020332120090420111243.0(CKB)5470000002396455(OCoLC)318793537(EXLCZ)99547000000239645520090420d1961 ua 0engurmn||||a||||txtrdacontentcrdamediacrrdacarrierReports and maps of the Geological Survey released only in the open files, 1960[electronic resource] /by Betsy A. Weld, Erwin S. Asselstine, and Arthur JohnsonWashington, D.C. :U.S. Dept. of the Interior, Geological Survey,1961.15 pages DJVU, image fileGeological Survey circular ;448Title from title screen (viewed Apr. 20, 2009).Includes index.Bibliographies.lcgftWeld Betsy A1388375Asselstine Erwin S1404858Johnson Arthur1389164Geological Survey (U.S.)GPOGPOBOOK9910698740203321Reports and maps of the Geological Survey released only in the open files, 19603512122UNINA03593nam 22005535 450 991015044700332120200630091115.03-319-47223-210.1007/978-3-319-47223-2(CKB)3710000000943225(DE-He213)978-3-319-47223-2(MiAaPQ)EBC4742103(PPN)197141242(EXLCZ)99371000000094322520161112d2016 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierHybrid Soft Computing for Image Segmentation /edited by Siddhartha Bhattacharyya, Paramartha Dutta, Sourav De, Goran Klepac1st ed. 2016.Cham :Springer International Publishing :Imprint: Springer,2016.1 online resource (XVI, 321 p. 162 illus., 87 illus. in color.)3-319-47222-4 Includes bibliographical references at the end of each chapters and index.Hybrid Soft Computing Techniques for Image Segmentation: Fundamentals and Applications -- Enhanced Rough-Fuzzy C-Means Algorithm for Image Segmentation -- Intuitionistic Fuzzy C-means Clustering Algorithm for Brain Image Segmentation -- Automatic Segmentation Approaches -- Modified Level Set Segmentation -- Fuzzy Deformable Models for 3D Segmentation of Brain Structures -- Rough Sets for Probabilistic Model Based Image Segmentation -- Segmentation of Cerebral Images. .This book proposes soft computing techniques for segmenting real-life images in applications such as image processing, image mining, video surveillance, and intelligent transportation systems. The book suggests hybrids deriving from three main approaches: fuzzy systems, primarily used for handling real-life problems that involve uncertainty; artificial neural networks, usually applied for machine cognition, learning, and recognition; and evolutionary computation, mainly used for search, exploration, efficient exploitation of contextual information, and optimization. The contributed chapters discuss both the strengths and the weaknesses of the approaches, and the book will be valuable for researchers and graduate students in the domains of image processing and computational intelligence.Artificial intelligenceComputational intelligenceOptical data processingArtificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Computational Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/T11014Computer Imaging, Vision, Pattern Recognition and Graphicshttps://scigraph.springernature.com/ontologies/product-market-codes/I22005Artificial intelligence.Computational intelligence.Optical data processing.Artificial Intelligence.Computational Intelligence.Computer Imaging, Vision, Pattern Recognition and Graphics.006.3Bhattacharyya Siddharthaedthttp://id.loc.gov/vocabulary/relators/edtDutta Paramarthaedthttp://id.loc.gov/vocabulary/relators/edtDe Souravedthttp://id.loc.gov/vocabulary/relators/edtKlepac Goranedthttp://id.loc.gov/vocabulary/relators/edtBOOK9910150447003321Hybrid Soft Computing for Image Segmentation1963598UNINA