LEADER 01355nam 2200361 n 450 001 996391042203316 005 20221108001720.0 035 $a(CKB)1000000000661376 035 $a(EEBO)2240937353 035 $a(UnM)99843874 035 $a(EXLCZ)991000000000661376 100 $a19910802d1602 uy | 101 0 $aeng 135 $aurbn||||a|bb| 200 00$aEirenarcha: or Of the office of the iustices of peace$b[electronic resource] $ein foure bookes. Reuised, corrected, and enlarged, in the fortie and fourth yeare of the peaceable raigne of our most gracious Queene Elizabeth. By William Lambard of Lincolnes Inne Gentleman 210 $aAt London $cPrinted by Thomas Wight$d1602 215 $a[2], 201, [2], 224-589, [85] p 300 $aAt foot of title: Cum priuilegio. 300 $aIncludes index. 300 $aThe last leaf is blank. 300 $aReproduction of the original in the British Library. 330 $aeebo-0018 606 $aJustices of the peace$zGreat Britain$vEarly works to 1800 615 0$aJustices of the peace 700 $aLambarde$b William$f1536-1601.$01002973 801 0$bCu-RivES 801 1$bCu-RivES 801 2$bCStRLIN 801 2$bWaOLN 906 $aBOOK 912 $a996391042203316 996 $aEirenarcha: or of the office of the iustices of peace$92306340 997 $aUNISA LEADER 05219nam 2200589 a 450 001 9910141726103321 005 20230803030115.0 010 $a1-118-61836-X 010 $a1-118-61838-6 010 $a1-118-61837-8 035 $a(CKB)2670000000359230 035 $a(EBL)1204058 035 $a(OCoLC)850163687 035 $a(MiAaPQ)EBC1204058 035 $a(DLC) 2013013075 035 $a(Au-PeEL)EBL1204058 035 $a(CaPaEBR)ebr10713664 035 $a(EXLCZ)992670000000359230 100 $a20130328d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aObject detection and recognition in digital images$b[electronic resource] $etheory and practice /$fBogus?aw Cyganek 210 $aChichester, West Sussex, U.K. $cJohn Wiley & Sons, Inc.$d2013 215 $a1 online resource (552 p.) 300 $aDescription based upon print version of record. 311 $a0-470-97637-3 320 $aIncludes bibliographical references and index. 327 $aOBJECT 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 Distances 327 $a2.5.1.1 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 Tensor 327 $a2.8 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) 327 $a2.12.3 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 Factorization 327 $a2.12.12 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 PCA 327 $a3.3.1.2 PCA for Multi-Channel Image Processing 330 $aObject 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. 606 $aPattern recognition systems 606 $aImage processing$xDigital techniques 606 $aComputer vision 615 0$aPattern recognition systems. 615 0$aImage processing$xDigital techniques. 615 0$aComputer vision. 676 $a621.39/94 700 $aCyganek$b Bogus?aw$0890978 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910141726103321 996 $aObject detection and recognition in digital images$92065456 997 $aUNINA