01292oam 2200373Ka 450 991069212690332120040120103949.0(CKB)5470000002352168(OCoLC)54030867ocm54030867(OCoLC)995470000002352168(EXLCZ)99547000000235216820040115d2002 ua 0engtxtrdacontentcrdamediacrrdacarrierSelecting materials to teach Spanish to Spanish speakers[electronic resource] /Paula Winke, Cathy StaffordWashington, DC :ERIC Clearinghouse on Languages and Linguistics,[2002]ERIC digestTitle from title screen (viewed on Jan. 15, 2004).Native language and educationSpanish languageStudy and teachingNative language and education.Spanish languageStudy and teaching.Winke Paula Marie1385414Stafford Cathy1385415ERIC Clearinghouse on Languages and Linguistics.GPOGPOBOOK9910692126903321Selecting materials to teach Spanish to Spanish speakers3432919UNINA05448nam 2200685 450 991082776110332120200520144314.00-12-802623-50-12-802419-4(CKB)3710000000308038(EBL)1888754(SSID)ssj0001551535(PQKBManifestationID)16169145(PQKBTitleCode)TC0001551535(PQKBWorkID)14812294(PQKB)10170778(Au-PeEL)EBL1888754(CaPaEBR)ebr10996811(CaONFJC)MIL785298(OCoLC)898422493(CaSebORM)9780128024195(MiAaPQ)EBC1888754(PPN)194315304(EXLCZ)99371000000030803820150106h20152015 uy 0engur|n|---|||||txtccrView-based 3-D object retrieval /Yue Gao, Qionghai Dai1st editionAmsterdam, Netherlands :Elsevier,2015.©20151 online resource (154 p.)Computer Science Reviews and TrendsDescription based upon print version of record.Includes bibliographical references.Front Cover; View-Based 3-D Object Retrieval; Copyright; Contents; Acknowledgments; Preface; Part I: The Start; Chapter 1: Introduction; 1.1 The Definition of 3DOR; 1.2 Model-Based 3DOR Versus View-Based 3DOR; 1.3 The Challenges of V3DOR; 1.4 Summary of Our Work; 1.4.1 View Extraction; 1.4.2 Representative View Selection; 1.4.3 Learning the Weights for Multiple Views; 1.4.4 Distance Measures for Object Matching; 1.4.5 Learning the Relevance Among 3-D Objects; 1.5 Structure of This Book; 1.6 Summary; References; Chapter 2: The Benchmark and Evaluation; 2.1 Introduction2.2 The Standard Benchmarks2.3 The Shape Retrieval Contest; 2.4 Evaluation Criteria in 3DOR; 2.5 Summary; References; Part II View Extraction, Selection, and Representation; Chapter 3: View Extraction; 3.1 Introduction; 3.2 Dense Sampling Viewpoints; 3.3 Predefined Camera Array; 3.4 Generated View; 3.5 Summary; References; Chapter 4: View Selection; 4.1 Introduction; 4.2 Unsupervised View Selection; 4.3 Interactive View Selection; 4.3.1 Multiview 3-D Object Matching; 4.3.2 View Clustering; 4.3.3 Initial Query View Selection; 4.3.4 Interactive View Selection with User Relevance Feedback4.3.5 Learning a Distance Metric4.3.6 Multiple Query Views Linear Combination; 4.3.7 The Computational Cost; 4.4 Summary; References; Chapter 5: View Representation; 5.1 Introduction; 5.2 Shape Feature Extraction; 5.2.1 Zernike Moments; 5.2.2 Fourier Descriptor; 5.3 The Bag-of-Visual-Features Method; 5.3.1 The Bag-of-Visual-Words; 5.3.2 The Bag-of-Region-Words; 5.4 Learning the Weights for Multiple Views; 5.4.1 K-Partite Graph Reinforcement; 5.4.2 Weight Learning for Multiple Views Usingthe k-Partite Graph; 5.5 Summary; References; Part III View-Based 3-D Object ComparisonChapter 6: Multiple-View Distance Metric6.1 Introduction; 6.2 Fundamental Many-to-Many Distance Measures; 6.3 Bipartite Graph Matching; 6.3.1 View Selection and Weighting; 6.3.2 Bipartite Graph Construction; 6.3.3 Bipartite Graph Matching; 6.4 Statistical Matching; 6.4.1 Adaptive View Clustering; 6.4.2 CCFV; 6.4.2.1 View Clustering and Query Model Training; 6.4.2.2 Positive and Negative Matching Models; 6.4.2.3 Calculation of the Similarity Between Q and O S(Q,O); 6.4.2.4 Analysis of Computational Cost; 6.4.3 Markov Chain; 6.4.4 Gaussian Mixture Model Formulation6.4.4.1 Conventional GMM Training6.4.4.2 Generative Adaptation of GMM; 6.4.4.3 Discriminative Adaptation of GMM; 6.4.4.4 Learning the Weights for Multiple GMMs; 6.5 Summary; References; Chapter 7: Learning-Based 3-D Object Retrieval; 7.1 Introduction; 7.2 Learning Optimal Distance Metrics; 7.2.1 Hausdorff Distance Learning; 7.2.2 Learning Bipartite Graph Optimal Matching; 7.3 3-D Object Relevance Estimation via Hypergraph Learning; 7.3.1 Hypergraph and Its Applications; 7.3.2 Learning on Single Hypergraph; 7.3.3 Learning on Multiple Hypergraphs7.3.4 Learning the Weights for Multiple Hypergraphs Content-based 3-D object retrieval has attracted extensive attention recently and has applications in a variety of fields, such as, computer-aided design, tele-medicine,mobile multimedia, virtual reality, and entertainment. The development of efficient and effective content-based 3-D object retrieval techniques has enabled the use of fast 3-D reconstruction and model design. Recent technical progress, such as the development of camera technologies, has made it possible to capture the views of 3-D objects. As a result, view-based 3-D object retrieval has become an essential but challenging resComputer Science Reviews and TrendsImage processingData processingPattern recognition systemsQuality controlImage processingData processing.Pattern recognition systemsQuality control.006.37Gao Yue994999Dai QionghaiMiAaPQMiAaPQMiAaPQBOOK9910827761103321View-based 3-D object retrieval4014077UNINA