LEADER 04113nam 22006135 450 001 9910799220603321 005 20240312133958.0 010 $a9783031345074 010 $a303134507X 024 7 $a10.1007/978-3-031-34507-4 035 $a(CKB)29476477200041 035 $a(DE-He213)978-3-031-34507-4 035 $a(MiAaPQ)EBC31051514 035 $a(Au-PeEL)EBL31051514 035 $a(MiAaPQ)EBC31042875 035 $a(Au-PeEL)EBL31042875 035 $a(OCoLC)1417196573 035 $a(EXLCZ)9929476477200041 100 $a20231227d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputer Vision: Three-dimensional Reconstruction Techniques /$fby Andrea Fusiello 205 $a1st ed. 2024. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2024. 215 $a1 online resource (XXIV, 338 p. 120 illus., 88 illus. in color.) 311 08$a9783031345067 311 08$a3031345061 320 $aIncludes bibliographical references and index. 327 $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. 330 $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. . 606 $aComputer vision 606 $aArtificial intelligence 606 $aInformation visualization 606 $aComputer Vision 606 $aArtificial Intelligence 606 $aData and Information Visualization 615 0$aComputer vision. 615 0$aArtificial intelligence. 615 0$aInformation visualization. 615 14$aComputer Vision. 615 24$aArtificial Intelligence. 615 24$aData and Information Visualization. 676 $a006.37 700 $aFusiello$b Andrea$0761627 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910799220603321 996 $aComputer Vision$93872329 997 $aUNINA