01084nam a22002411i 450099100189459970753620040104153559.0040407s1968 fr |||||||||||||||||fre b1283953x-39ule_instARCHE-081915ExLDip.to Scienze StoricheitaA.t.i. Arché s.c.r.l. Pandora Sicilia s.r.l.417.7Nelli, René403185Écritures cathares :la cène secrète, le livre des deux principes, traité cathare, le rituel occitan, le rituel latin ; textes précathares et cathares préséntes, traduits et commentés avec une introduction sur les origines et l'Esprit du Catharisme /par René NelliParis :Planete,1968252 p. ;21 cmPaleografia.b1283953x02-04-1416-04-04991001894599707536LE009 STOR.10.0-1412009000108134le009-E0.00-no 00000.i1339280316-04-04Écritures cathares297352UNISALENTOle00916-04-04ma -frefr 0105219nam 2200589 a 450 991014172610332120230803030115.01-118-61836-X1-118-61838-61-118-61837-8(CKB)2670000000359230(EBL)1204058(OCoLC)850163687(MiAaPQ)EBC1204058(DLC) 2013013075(Au-PeEL)EBL1204058(CaPaEBR)ebr10713664(EXLCZ)99267000000035923020130328d2013 uy 0engur|n|---|||||rdacontentrdamediardacarrierObject detection and recognition in digital images[electronic resource] theory and practice /Bogusław CyganekChichester, West Sussex, U.K. John Wiley & Sons, Inc.20131 online resource (552 p.)Description based upon print version of record.0-470-97637-3 Includes bibliographical references and index.OBJECT DETECTION AND RECOGNITION IN DIGITAL IMAGES; Contents; Preface; Acknowledgements; Notations and Abbreviations; 1 Introduction; 1.1 A Sample of Computer Vision; 1.2 Overview of Book Contents; References; 2 Tensor Methods in Computer Vision; 2.1 Abstract; 2.2 Tensor - A Mathematical Object; 2.2.1 Main Properties of Linear Spaces; 2.2.2 Concept of a Tensor; 2.3 Tensor - A Data Object; 2.4 Basic Properties of Tensors; 2.4.1 Notation of Tensor Indices and Components; 2.4.2 Tensor Products; 2.5 Tensor Distance Measures; 2.5.1 Overview of Tensor Distances2.5.1.1 Computation of Matrix Exponent and Logarithm Functions2.5.2 Euclidean Image Distance and Standardizing Transform; 2.6 Filtering of Tensor Fields; 2.6.1 Order Statistic Filtering of Tensor Data; 2.6.2 Anisotropic Diffusion Filtering; 2.6.3 IMPLEMENTATION of Diffusion Processes; 2.7 Looking into Images with the Structural Tensor; 2.7.1 Structural Tensor in Two-Dimensional Image Space; 2.7.2 Spatio-Temporal Structural Tensor; 2.7.3 Multichannel and Scale-Space Structural Tensor; 2.7.4 Extended Structural Tensor; 2.7.4.1 IMPLEMENTATION of the Linear and Nonlinear Structural Tensor2.8 Object Representation with Tensor of Inertia and Moments2.8.1 IMPLEMENTATION of Moments and their Invariants; 2.9 Eigendecomposition and Representation of Tensors; 2.10 Tensor Invariants; 2.11 Geometry of Multiple Views: The Multifocal Tensor; 2.12 Multilinear Tensor Methods; 2.12.1 Basic Concepts of Multilinear Algebra; 2.12.1.1 Tensor Flattening; 2.12.1.2 IMPLEMENTATION Tensor Representation; 2.12.1.3 The k-mode Product of a Tensor and a Matrix; 2.12.1.4 Ranks of a Tensor; 2.12.1.5 IMPLEMENTATION of Basic Operations on Tensors; 2.12.2 Higher-Order Singular Value Decomposition (HOSVD)2.12.3 Computation of the HOSVD2.12.3.1 Implementation of the HOSVD Decomposition; 2.12.4 HOSVD Induced Bases; 2.12.5 Tensor Best Rank-1 Approximation; 2.12.6 Rank-1 Decomposition of Tensors; 2.12.7 Best Rank-(R1, R2, . . . , RP) Approximation; 2.12.8 Computation of the Best Rank-(R1, R2, . . . , RP) Approximations; 2.12.8.1 IMPLEMENTATION - Rank Tensor Decompositions; 2.12.8.2 CASE STUDY - Data Dimensionality Reduction; 2.12.9 Subspace Data Representation; 2.12.10 Nonnegative Matrix Factorization; 2.12.11 Computation of the Nonnegative Matrix Factorization2.12.12 Image Representation with NMF2.12.13 Implementation of the Nonnegative Matrix Factorization; 2.12.14 Nonnegative Tensor Factorization; 2.12.15 Multilinear Methods of Object Recognition; 2.13 Closure; 2.13.1 Chapter Summary; 2.13.2 Further Reading; 2.13.3 Problems and Exercises; References; 3 Classification Methods and Algorithms; 3.1 Abstract; 3.2 Classification Framework; 3.2.1 IMPLEMENTATION Computer Representation of Features; 3.3 Subspace Methods for Object Recognition; 3.3.1 Principal Component Analysis; 3.3.1.1 Computation of the PCA3.3.1.2 PCA for Multi-Channel Image ProcessingObject detection, tracking and recognition in images are key problems in computer vision. This book provides the reader with a balanced treatment between the theory and practice of selected methods in these areas to make the book accessible to a range of researchers, engineers, developers and postgraduate students working in computer vision and related fields. Key features: Explains the main theoretical ideas behind each method (which are augmented with a rigorous mathematical derivation of the formulas), their implementation (in C++) and demonstrated working in real applications.Pattern recognition systemsImage processingDigital techniquesComputer visionPattern recognition systems.Image processingDigital techniques.Computer vision.621.39/94Cyganek Bogusław890978MiAaPQMiAaPQMiAaPQBOOK9910141726103321Object detection and recognition in digital images2065456UNINA