LEADER 05482nam 2200697 450 001 9910459628303321 005 20200520144314.0 010 $a0-12-802623-5 010 $a0-12-802419-4 035 $a(CKB)3710000000308038 035 $a(EBL)1888754 035 $a(SSID)ssj0001551535 035 $a(PQKBManifestationID)16169145 035 $a(PQKBTitleCode)TC0001551535 035 $a(PQKBWorkID)14812294 035 $a(PQKB)10170778 035 $a(MiAaPQ)EBC1888754 035 $a(CaSebORM)9780128024195 035 $a(PPN)194315304 035 $a(Au-PeEL)EBL1888754 035 $a(CaPaEBR)ebr10996811 035 $a(CaONFJC)MIL785298 035 $a(OCoLC)898422493 035 $a(EXLCZ)993710000000308038 100 $a20150106h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aView-based 3-D object retrieval /$fYue Gao, Qionghai Dai 205 $a1st edition 210 1$aAmsterdam, Netherlands :$cElsevier,$d2015. 210 4$dİ2015 215 $a1 online resource (154 p.) 225 0 $aComputer Science Reviews and Trends 300 $aDescription based upon print version of record. 320 $aIncludes bibliographical references. 327 $aFront 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 Introduction 327 $a2.2 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 Feedback 327 $a4.3.5 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 Comparison 327 $aChapter 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 Formulation 327 $a6.4.4.1 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 Hypergraphs 327 $a7.3.4 Learning the Weights for Multiple Hypergraphs 330 $a Content-based 3-D object retrieval has attracted extensive attention recently and has applications in a variety of fields, such as, computer-aided design, tele-medicine,mobile multimedia, virtual reality, and entertainment. The development of efficient and effective content-based 3-D object retrieval techniques has enabled the use of fast 3-D reconstruction and model design. Recent technical progress, such as the development of camera technologies, has made it possible to capture the views of 3-D objects. As a result, view-based 3-D object retrieval has become an essential but challenging res 410 0$aComputer Science Reviews and Trends 606 $aImage processing$xData processing 606 $aPattern recognition systems$xQuality control 608 $aElectronic books. 615 0$aImage processing$xData processing. 615 0$aPattern recognition systems$xQuality control. 676 $a006.37 700 $aGao$b Yue$0994999 702 $aDai$b Qionghai 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910459628303321 996 $aView-based 3-D object retrieval$92279159 997 $aUNINA LEADER 01726nam 2200409 450 001 996279861003316 005 20231205232201.0 010 $a0780310604 010 $a0-7381-1060-4 035 $a(CKB)1000000000035313 035 $a(NjHacI)991000000000035313 035 $a(EXLCZ)991000000000035313 100 $a20231205d1986 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aANSI/IEEE C37.24-1986 (Revision of ANSI/IEEE C37.24-1971) /$fIEEE 210 1$a[Place of publication not identified] :$cIEEE,$d1986. 215 $a1 online resource 311 $a0780310604 311 $a0780310604 330 $aSuperseded by C37.24-2003. This IEEE Standards product is part of the C37 family on Switchgear, Substations and Protective Relays. This standard applies to all forms of outdoor metal-enclosed switchgear. 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