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010   $a3-319-52483-6
024 7 $a10.1007/978-3-319-52483-2
035   $a(CKB)3710000001127266
035   $a(DE-He213)978-3-319-52483-2
035   $a(MiAaPQ)EBC6312641
035   $a(MiAaPQ)EBC5577026
035   $a(Au-PeEL)EBL5577026
035   $a(OCoLC)979415041
035   $a(PPN)199767815
035   $a(EXLCZ)993710000001127266
100   $a20170318d2017      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aFoundations of Computer Vision $eComputational Geometry, Visual Image Structures and Object Shape Detection /$fby James F. Peters
205   $a1st ed. 2017.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017.
215   $a1 online resource (XVII, 431 p. 354 illus., 301 illus. in color.) 
225 1 $aIntelligent Systems Reference Library,$x1868-4394 ;$v124
311   $a3-319-52481-X 
320   $aIncludes bibliographical references and index.
327   $aBasics Leading to Machine Vision -- Working with Pixels -- Visualising Pixel Intensity Distributions -- Linear Filtering -- Edges, Lines, Corners, Gaussian kernel and Voronoļ Meshes -- Delaunay Mesh Segmentation -- Video Processing. An Introduction to Real-Time and O?ine Video Analysis -- Lowe Keypoints, Maximal Nucleus Clusters, Contours and Shapes -- Postscript. Where Do Shapes ?t into the Computer Vision Landscape?.
330   $aThis book introduces the fundamentals of computer vision (CV), with a focus on extracting useful information from digital images and videos. Including a wealth of methods used in detecting and classifying image objects and their shapes, it is the first book to apply a trio of tools (computational geometry, topology and algorithms) in solving CV problems, shape tracking in image object recognition and detecting the repetition of shapes in single images and video frames. Computational geometry provides a visualization of topological structures such as neighborhoods of points embedded in images, while image topology supplies us with structures useful in the analysis and classi?cation of image regions. Algorithms provide a practical, step-by-step means of viewing image structures. The implementations of CV methods in Matlab and Mathematica, classi?cation of chapter problems with the symbols (easily solved) and (challenging) and its extensive glossary of key words, examples and connections with the fabric of CV make the book an invaluable resource for advanced undergraduate and ?rst year graduate students in Engineering, Computer Science or Applied Mathematics. It offers insights into the design of CV experiments, inclusion of image processing methods in CV projects, as well as the reconstruction and interpretation of recorded natural scenes.
410  0$aIntelligent Systems Reference Library,$x1868-4394 ;$v124
606   $aComputational intelligence
606   $aOptical data processing
606   $aArtificial intelligence
606   $aPhysics
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
606   $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aApplications of Graph Theory and Complex Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/P33010
615  0$aComputational intelligence.
615  0$aOptical data processing.
615  0$aArtificial intelligence.
615  0$aPhysics.
615 14$aComputational Intelligence.
615 24$aImage Processing and Computer Vision.
615 24$aArtificial Intelligence.
615 24$aApplications of Graph Theory and Complex Networks.
676   $a006.37
700   $aPeters$b James F$4aut$4http://id.loc.gov/vocabulary/relators/aut$0555283
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910254342903321
996   $aFoundations of Computer Vision$92041869
997   $aUNINA