02962nam 2200457 450 991079338400332120230607203913.01-5017-2411-810.7591/9781501724114(CKB)4100000007109317(DE-B1597)514827(OCoLC)1083627780(DE-B1597)9781501724114(MiAaPQ)EBC5965017(Au-PeEL)EBL5965017(EXLCZ)99410000000710931720201108d2002 uy 0engur||#||||||||txtrdacontentcrdamediacrrdacarrierThe ethics of transracial adoption /Hawley Fogg-DavisIthaca ;London :Cornell University Press,[2002]©20021 online resource0-8014-3898-5 Includes bibliographical references (pages 137-145) and index.Front matter --Contents --Acknowledgments --Introduction --CHAPTER 1. Navigating Racial Meaning --CHAPTER 2. What's Wrong with Colorblindness? --CHAPTER 3. What's Wrong with Racial Solidity? --CHAPTER 4. Racial Randomization --CHAPTER 5. Navigating the Involuntary Association of TRA --Conclusion --Notes --Bibliography --Cases --IndexTransracial adoption is one of the most contentious issues in adoption politics and in the politics of race more generally. Some who support transracial adoption use a theory of colorblindness, while many who oppose it draw a causal connection between race and culture and argue that a black child's racial and cultural interests are best served by black adoptive parents. Hawley Fogg-Davis carves out a middle ground between these positions. She believes that race should not be a barrier to adoption, but neither should it be absent from the minds of prospective adopters and adoption practitioners. Fogg-Davis's argument in favor of transracial adoption is based on the moral and legal principle of nondiscrimination and a theory of race-consciousness she terms "racial navigation." Challenging the notion that children "get" their racial identity from their parents, she argues that children, through the process of racial navigation, should cultivate their self-identification in dialogue with others. The Ethics of Transracial Adoption explores new ground in the transracial adoption debate by examining the relationship between personal and public conceptions of race and racism before, during, and after adoption.Interracial adoptionMoral and ethical aspectsUnited StatesInterracial adoptionMoral and ethical aspects362.7340973Fogg-Davis Hawley Grace1970-1527671MiAaPQMiAaPQMiAaPQBOOK9910793384003321The ethics of transracial adoption3770710UNINA05358nam 2200661 a 450 991081546890332120240313224558.09781118618363111861836X9781118618387111861838697811186183701118618378(CKB)2670000000359230(EBL)1204058(OCoLC)850163687(MiAaPQ)EBC1204058(DLC) 2013013075(Au-PeEL)EBL1204058(CaPaEBR)ebr10713664(Perlego)1002087(EXLCZ)99267000000035923020130328d2013 uy 0engur|n|---|||||rdacontentrdamediardacarrierObject detection and recognition in digital images theory and practice /Bogusław Cyganek1st ed.Chichester, West Sussex, U.K. John Wiley & Sons, Inc.20131 online resource (552 p.)Description based upon print version of record.9780470976371 0470976373 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 Bogusław890978MiAaPQMiAaPQMiAaPQBOOK9910815468903321Object detection and recognition in digital images4114502UNINA