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020 _a9783031345074
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024 7 _a10.1007/978-3-031-34507-4
_2doi
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_2bicssc
072 7 _aCOM016000
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_223
100 1 _aFusiello, Andrea.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_995585
245 1 0 _aComputer Vision: Three-dimensional Reconstruction Techniques
_h[electronic resource] /
_cby Andrea Fusiello.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2024.
300 _aXXIV, 338 p. 120 illus., 88 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aForeword -- Preface -- Acknowledgements -- Introduction -- Fundamentals of Imaging -- The Pinhole Camera Model -- Camera Calibration -- Absolute and Exterior Orientation -- Two-view Geometry -- Relative Orientation -- Reconstruction from Two Images -- Nonlinear Regression -- Stereopsis: geometry -- Stereopsis: matching -- Renge Sensors -- Multiview Euclidean Reconstruction -- 3D Registration -- Multiview Projective Reconstruction and Autocalibration -- Multi-View Stereo Reconstruction -- Image-based Rendering -- A Notions of linear algebra -- B Matrix Differential Calculation -- C Regression -- D Notions of Projective Geometry -- D Math Lab code -- Index.
520 _aFrom facial recognition to self-driving cars, the applications of computer vision are vast and ever-expanding. Geometry plays a fundamental role in this discipline, providing the necessary mathematical framework to understand the underlying principles of how we perceive and interpret visual information in the world around us. This text explores the theories and computational techniques used to determine the geometric properties of solid objects through images. It covers the basic concepts and provides the necessary mathematical background for more advanced studies. The book is divided into clear and concise chapters covering a wide range of topics including image formation, camera models, feature detection and 3D reconstruction. Each chapter includes detailed explanations of the theory as well as practical examples to help the reader understand and apply the concepts presented. The book has been written with the intention of being used as a primary resourcefor students on university courses in computer vision, particularly final year undergraduate or postgraduate computer science or engineering courses. It is also useful for self-study and for those who, outside the academic field, find themselves applying computer vision to solve practical problems. The aim of the book is to strike a balance between the complexity of the theory and its practical applicability in terms of implementation. Rather than providing a comprehensive overview of the current state of the art, it offers a selection of specific methods with enough detail to enable the reader to implement them. .
650 0 _aComputer vision.
_995588
650 0 _aArtificial intelligence.
_93407
650 0 _aInformation visualization.
_914255
650 1 4 _aComputer Vision.
_995590
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aData and Information Visualization.
_933848
710 2 _aSpringerLink (Online service)
_995593
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031345067
776 0 8 _iPrinted edition:
_z9783031345081
776 0 8 _iPrinted edition:
_z9783031345098
856 4 0 _uhttps://doi.org/10.1007/978-3-031-34507-4
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
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