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001440115 001__ 1440115
001440115 003__ OCoLC
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001440115 019__ $$a1287764050$$a1292517945
001440115 020__ $$a9783030873554$$q(electronic bk.)
001440115 020__ $$a3030873552$$q(electronic bk.)
001440115 0247_ $$a10.1007/978-3-030-87355-4$$2doi
001440115 035__ $$aSP(OCoLC)1272994269
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001440115 049__ $$aISEA
001440115 050_4 $$aT385$$b.I585 2021eb
001440115 08204 $$a006.6$$223
001440115 1112_ $$aInternational Conference on Image and Graphics$$n(11th :$$d2021 :$$cHaikou Shi, China)
001440115 24510 $$aImage and graphics :$$b11th International Conference, ICIG 2021, Haikou, China, August 6-8, 2021, Proceedings.$$nPart I /$$cYuxin Peng, Shi-Min Hu, Moncef Gabbouj, Kun Zhou, Michael Elad, Kun Xu (eds.).
001440115 2463_ $$aICIG 2021
001440115 264_1 $$aCham :$$bSpringer,$$c2021.
001440115 300__ $$a1 online resource (830 pages)
001440115 336__ $$atext$$btxt$$2rdacontent
001440115 337__ $$acomputer$$bc$$2rdamedia
001440115 338__ $$aonline resource$$bcr$$2rdacarrier
001440115 347__ $$atext file
001440115 347__ $$bPDF
001440115 4901_ $$aLecture notes in computer science ;$$v12888
001440115 4901_ $$aLNCS sublibrary, SL 6, Image processing, computer vision, pattern recognition, and graphics
001440115 5050_ $$aIntro -- Preface -- Organization -- Contents -- Part I -- Contents -- Part II -- Contents -- Part III -- Object Detection and Recognition -- L2-CVAEGAN: Feature Aligned Generative Networks for Zero-Shot Learning -- 1 Introduction -- 2 Related Work -- 2.1 Zero-Shot Learning -- 2.2 Generative Models -- 3 Methodology -- 3.1 Setup -- 3.2 Model Overview -- 3.3 Our Proposed Method -- 3.4 Evaluation Protocol -- 4 Experiments -- 4.1 Settings -- 4.2 Comparing with Different Methods -- 4.3 Visualization and Analyzing -- 4.4 Generalized Zero-Shot Learning -- 4.5 Effect of Semantic Combination
001440115 5058_ $$a5 Conclusion -- References -- HQ-Trans: A High-Quality Screening Based Image Translation Framework for Unsupervised Cross-Domain Pedestrian Detection -- 1 Introduction -- 2 Related Work -- 2.1 Image Translation -- 2.2 Object Detection -- 2.3 Unsupervised Pedestrian Detection -- 3 Method -- 3.1 The First Screening -- 3.2 Scene Translation -- 3.3 The Second Screening -- 3.4 Pedestrian Detector -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Experimental Results and Analysis -- 4.3 Comparisons with Other Works -- 5 Conclusion -- References
001440115 5058_ $$aFER-YOLO: Detection and Classification Based on Facial Expressions -- 1 Introduction -- 2 Methodology -- 2.1 Model Architecture -- 2.2 Channel Attention -- 2.3 Depth-Wise Separable Convolution -- 3 Experimental Results and Analysis -- 3.1 Implementation Details -- 3.2 Ablation Experiments -- 3.3 Comparisons with State-of the Art Methods -- 3.4 Real-Time Facial Expression Detection via Camera -- 4 Conclusion -- References -- MSC-Fuse: An Unsupervised Multi-scale Convolutional Fusion Framework for Infrared and Visible Image -- 1 Introduction -- 2 Proposed Fusion Method -- 2.1 Network Architecture
001440115 5058_ $$a2.2 The Basic Convolution Blocks -- 2.3 Loss Function -- 3 Experimental Results -- 3.1 Experimental Environment and Parameter Setting -- 3.2 Ablation Study -- 3.3 Compared with Other Methods -- 4 Conclusions -- References -- Relation-Aware Reasoning with Graph Convolutional Network -- 1 Introduction -- 2 Related Works -- 2.1 Knowledge Graph Based Methods -- 2.2 Relational Reasoning -- 3 Proposed Framework -- 3.1 Knowledge Graph Construction -- 3.2 GCN for Visual Reasoning -- 3.3 Implementation Details -- 4 Experiments -- 4.1 Datasets and Evaluation -- 4.2 Experimental Results
001440115 5058_ $$a4.3 Ablation Study -- 5 Conclusion -- References -- Feature Separation GAN for Cross View Gait Recognition -- 1 Introduction -- 2 Related Work -- 2.1 Generative Adversarial Networks -- 2.2 Generative Method for Gait Recognition -- 2.3 Gait Energy Image -- 3 Proposed Method -- 3.1 Network Structure -- 3.2 Separation Features -- 3.3 Constraint Block -- 3.4 Generator Optimizing -- 4 Experiment Result -- 4.1 Data Set -- 4.2 Implementation Details -- 4.3 Experimental Result -- 5 Conclusion -- References -- Global Feature Polishing Network for Glass-Like Object Detection -- 1 Introduction -- 2 Related Work
001440115 506__ $$aAccess limited to authorized users.
001440115 520__ $$aThis three-volume set LNCS 12888, 12898, and 12890 constitutes the refereed conference proceedings of the 11th International Conference on Image and Graphics, ICIG 2021, held in Haikou, China, in August 2021.* The 198 full papers presented were selected from 421 submissions and focus on advances of theory, techniques and algorithms as well as innovative technologies of image, video and graphics processing and fostering innovation, entrepreneurship, and networking. *The conference was postponed due to the COVID-19 pandemic.
001440115 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 7, 2021).
001440115 650_0 $$aComputer graphics$$vCongresses.
001440115 650_0 $$aImage processing$$xDigital techniques$$vCongresses.
001440115 650_6 $$aInfographie$$vCongrès.
001440115 650_6 $$aTraitement d'images$$xTechniques numériques$$vCongrès.
001440115 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001440115 655_7 $$aConference papers and proceedings.$$2lcgft
001440115 655_7 $$aActes de congrès.$$2rvmgf
001440115 655_0 $$aElectronic books.
001440115 7001_ $$aPeng, Yuxin.
001440115 7001_ $$aHu, Shi-Min.
001440115 7001_ $$aGabbouj, Moncef.
001440115 7001_ $$aZhou, Kun.
001440115 7001_ $$aElad, M.$$q(Michael)
001440115 7001_ $$aXu, Kun.
001440115 77608 $$iPrint version:$$aPeng, Yuxin.$$tImage and Graphics.$$dCham : Springer International Publishing AG, ©2021$$z9783030873547
001440115 830_0 $$aLecture notes in computer science ;$$v12888.
001440115 830_0 $$aLNCS sublibrary.$$nSL 6,$$pImage processing, computer vision, pattern recognition, and graphics.
001440115 852__ $$bebk
001440115 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-87355-4$$zOnline Access$$91397441.1
001440115 909CO $$ooai:library.usi.edu:1440115$$pGLOBAL_SET
001440115 980__ $$aBIB
001440115 980__ $$aEBOOK
001440115 982__ $$aEbook
001440115 983__ $$aOnline
001440115 994__ $$a92$$bISE